{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91c89f62",
   "metadata": {},
   "source": [
    "### 命令行模式\n",
    "**Est键：**进入命令行模式（单元格变蓝）\n",
    "**Ctrl+Enter：**运行单元格并停留在此\n",
    "**Alt+Enter：**运行单元格并在下面插入一个单元格\n",
    "**Shift+Entre：**运行单元格并选择下一个单元格\n",
    "**Tab：**代码补全\n",
    "**A：**上方插入单元格\n",
    "**B：**下面插入单元格\n",
    "**M：**单元格变成Markdown\n",
    "**Y：**单元格变成代码块\n",
    "**DD：**删除选中单元格\n",
    "### 编辑模式\n",
    "**Enter：**进入编辑模式\n",
    "**Ctrl+/：**代码注释\n",
    "**Ctrl+D：**删除整行"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
    },
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"
    }
   },
   "cell_type": "markdown",
   "id": "ce771f8d",
   "metadata": {},
   "source": [
    "### KNN(K-Nearest Neighbor)\n",
    "寻找最近的K个数据，推测数据的分类\n",
    "![image.png](attachment:image.png)\n",
    "1. 通用步骤\n",
    "  * 计算距离（欧氏距离或马氏距离）\n",
    "  * 升序排列\n",
    "  * 取前K个数据\n",
    "  * 加权平均：\n",
    "  越近越影响大，越远影响越小\n",
    "2. K的选取\n",
    "  * **太大：**导致分类模糊\n",
    "  * **太小：**波动大，不够精确\n",
    "3. 如何选取K\n",
    "  * 经验\n",
    "  * 均方根误差\n",
    "  ![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 664,
   "id": "f387c934",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "iris_X = iris.data\n",
    "iris_y = iris.target\n",
    "\n",
    "#分割数据集（打乱顺序）,30%的测试比例\n",
    "X_train,X_test,y_train,y_test = train_test_split(\n",
    "iris_X,iris_y,test_size = 0.3\n",
    ")\n",
    "\n",
    "#数据可视化（失败）\n",
    "# plt.subplot(1,2,1)\n",
    "# plt.scatter(X_train,y_train,\"ro\")\n",
    "# plt.xlabel(\"X_train\",fontsize = 10)\n",
    "\n",
    "#计算knn距离\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 619,
   "id": "67bed52f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0 0 2 2 2 0 0 2 1 0 1 2 0 0 1 2 2 2 1 2 0 1 1 2 0 1 1 0 1 1 2 0 0 0 0 2 2\n",
      " 0 2 1 2 0 0 1 0]\n",
      "[0 0 2 2 2 0 0 2 1 0 1 2 0 0 1 2 2 2 1 2 0 1 2 2 0 2 1 0 1 1 2 0 0 0 0 2 2\n",
      " 0 2 1 2 0 0 1 0]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "iris_X = iris.data\n",
    "iris_y = iris.target\n",
    "\n",
    "# print(iris_X[:2])\n",
    "# print(iris_Y)\n",
    " \n",
    "#分割数据集（打乱顺序）,30%的测试比例\n",
    "X_train,X_test,y_train,y_test = train_test_split(\n",
    "iris_X,iris_y,test_size = 0.3\n",
    ")\n",
    "\n",
    "# print(y_test)\n",
    "\n",
    "#knn算法\n",
    "knn = KNeighborsClassifier()\n",
    "#训练数据\n",
    "knn.fit(X_train,y_train)\n",
    "#算法已经训练完成，开始预测数据\n",
    "print(knn.predict(X_test))\n",
    "print(y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 620,
   "id": "c01f350f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-1.08011358e-01  4.64204584e-02  2.05586264e-02  2.68673382e+00\n",
      " -1.77666112e+01  3.80986521e+00  6.92224640e-04 -1.47556685e+00\n",
      "  3.06049479e-01 -1.23345939e-02 -9.52747232e-01  9.31168327e-03\n",
      " -5.24758378e-01]\n",
      "36.45948838508993\n",
      "{'copy_X': True, 'fit_intercept': True, 'n_jobs': None, 'normalize': 'deprecated', 'positive': False}\n",
      "0.7406426641094095\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\edge\\Anaconda-install\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n",
      "\n",
      "    The Boston housing prices dataset has an ethical problem. You can refer to\n",
      "    the documentation of this function for further details.\n",
      "\n",
      "    The scikit-learn maintainers therefore strongly discourage the use of this\n",
      "    dataset unless the purpose of the code is to study and educate about\n",
      "    ethical issues in data science and machine learning.\n",
      "\n",
      "    In this special case, you can fetch the dataset from the original\n",
      "    source::\n",
      "\n",
      "        import pandas as pd\n",
      "        import numpy as np\n",
      "\n",
      "\n",
      "        data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n",
      "        raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n",
      "        data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n",
      "        target = raw_df.values[1::2, 2]\n",
      "\n",
      "    Alternative datasets include the California housing dataset (i.e.\n",
      "    :func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n",
      "    dataset. You can load the datasets as follows::\n",
      "\n",
      "        from sklearn.datasets import fetch_california_housing\n",
      "        housing = fetch_california_housing()\n",
      "\n",
      "    for the California housing dataset and::\n",
      "\n",
      "        from sklearn.datasets import fetch_openml\n",
      "        housing = fetch_openml(name=\"house_prices\", as_frame=True)\n",
      "\n",
      "    for the Ames housing dataset.\n",
      "    \n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "#导入数据\n",
    "loaded_data = datasets.load_boston()\n",
    "#属性\n",
    "data_X = loaded_data.data\n",
    "#标签\n",
    "data_y = loaded_data.target\n",
    "\n",
    "#导入模型（线性回归），训练数据\n",
    "model = LinearRegression()\n",
    "model.fit(data_X,data_y)\n",
    "\n",
    "#预测模型\n",
    "#print(model.predict(data_X[:6]))\n",
    "#print(data_y[:6])\n",
    "\n",
    "#各个维的斜率\n",
    "print(model.coef_)\n",
    "#于y轴的交点\n",
    "print(model.intercept_)\n",
    "#获取参数\n",
    "print(model.get_params())\n",
    "#准确率评估\n",
    "print(model.score(data_X,data_y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 621,
   "id": "4fc01cdd",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn import datasets\n",
    "from sklearn.linear_model import LinearRegression\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#创建数据(一个特征，一个标签，设置无序性)\n",
    "X,y = datasets.make_regression(n_samples = 100,n_features = 1,n_targets = 1,noise = 1)\n",
    "plt.scatter(X,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4bbe6ae2",
   "metadata": {},
   "source": [
    "### 数据标准化的原因\n",
    "**归一化，标准化（normalization)：**\n",
    "不同的评价指标具有不同的量纲单位，为了消除指标之间的量纲影响，提高机器学习的效率，需要对数据进行标准化处理\n",
    "#### 如何数据标准化\n",
    " **1.min-max标准化（极差标准化）：**\n",
    "运用公式：$$ b_{ij}=\\frac{a_{*J}-min(a_{*J})}{max(a_{*j})- mim(a_{*J})}$$ \n",
    "假设原本的矩阵为:\n",
    "$$\\begin{bmatrix}\n",
    "a_{11} & a_{12} & \\cdots \\\\\n",
    "a_{21} & a_{22} & \\cdots \\\\\n",
    "a_{31} & a_{32} & \\cdots \\\\\n",
    "\\vdots & \\vdots & \\ddots \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "经过标准化变为:\n",
    "$$\\begin{bmatrix}\n",
    "b_{11} & b_{12} & \\cdots \\\\\n",
    "b_{21} & b_{22} & \\cdots \\\\\n",
    "b_{31} & b_{32} & \\cdots \\\\\n",
    "\\vdots & \\vdots & \\ddots \\\\\n",
    "\\end{bmatrix}\n",
    "$$\n",
    "从而压缩数据。\n",
    "**2.Box-Cox变换：**\n",
    "响应数据不满足正态分布的情况，经过Box0-Cox变换之后，可以一定程度上减小不可观测的误差和预测变量相关性，从而改善数据的正态性、堆成性和方差性\n",
    "数学公式如下：\n",
    "$$y(\\ $$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 665,
   "id": "a63179b0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[643 316 317 895]\n",
      " [482 106 337 720]\n",
      " [401 285 278 464]\n",
      " [640 409 526 208]]\n",
      "[[0.61555312 0.30411827 0.29113924 0.9129979 ]\n",
      " [0.43921139 0.08236536 0.31223629 0.72955975]\n",
      " [0.35049288 0.27138332 0.25       0.46121593]\n",
      " [0.61226725 0.40232313 0.51160338 0.19287212]]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#极差标准代码实现\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib;matplotlib.rc(\"font\",family = \"Microsoft YaHei\")\n",
    "# A = np.array([[1000,10],\n",
    "#             [765,5],\n",
    "#             [-800,7],\n",
    "#             [0,1]],\n",
    "#           )\n",
    "# print(A)\n",
    "\n",
    "#验证任何数组都能用这个模（创建一个随机整型数组）\n",
    "A = np.random.randint(1,1000,(40,4))\n",
    "\n",
    "#计算有效列\n",
    "s = 0\n",
    "for j in range(A.shape[1]):     #shape[1]计算列的数量\n",
    "    if A[:,j].max() - A[:,j].min() != 0:\n",
    "        s += 1\n",
    "    else:\n",
    "        pass\n",
    "print(A[:4,:])\n",
    "if s == A.shape[1]:\n",
    "    #创建一个大小和A一样的空矩阵\n",
    "    B = np.zeros(shape = (A.shape[0] ,A.shape[1]))\n",
    "    for i in range(A.shape[0]):\n",
    "        for j in range(A.shape[1]):\n",
    "            B[i,j] = (A[i,j] - A[:,j].min())/(A[:,j].max() - A[:,j].min())\n",
    "    print(B[:4,:])\n",
    "    #原始数据可视化\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.plot(A[:,0],A[:,1],\"ro\")\n",
    "    plt.xlabel('原始数据矩阵A',fontsize = 10)\n",
    "    #标准化数据可视化\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.plot(B[:,0],B[:,1],\"go\")\n",
    "    plt.xlabel('标准化数据B',fontsize = 10)\n",
    "else:\n",
    "    print(\"原始数据据\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 623,
   "id": "18a73aa9",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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hTOXzd12ZMcI7FZCouEhufLo3r095noEj7iw3hw+Acxr4D5G7gtVkclOj5lJi4vRXkOnJGfi8+vn/+7YcKFXzTCYTQ354EluEDYtNkw52RNlpcuE5XHlvaNipLJD+g8jsSUjnNKSaqX+QEq6Rugqi5Km43fp3pkbDalgdp0JU9kgb1z9xNVVqlFwjqbhI6YTMXA4fAB/ITGTWmPIyq1JjrPQN8kV6/ib4C3sSE3jXgbl2yCsxVaID+xKhsg61GpVsRavHBV1b8PWOj5nzv0WkHEnlgq4tuPDK1qdl81LN/AQyP0fT71EACXGfImydgo4Tkfcj04fk0cexgv0KhBJVYjusdisfLnmL3z+fzaKflhIZG0HvR66k47W6lfinD99/gF7oygfupafbGgMMp19p8Xl9LP5lOUumrCCmSjS97uvGua11VC2VGoCFEAcuBCiJunObLWZueu46fnjnt6AQjy3Cyt1v3FJ6byIXCdXjufnZ68pk7nBIzzrIHEPOU1AgnVSmPgxVlyFELu0k+zXg3wuZX4Awa8qXlmbg24t6uCWYqkHkIJSI4kspOCLt3Ph0b258ugLJMShVtPeqhxHTLxcMp18J8Xq8PNvtdXau0ySNFZPCrAnzeeTDe+h1b7egY0XELcjsbwl2+opW3JSPkFf/wddjj7Txw7DJpB3PoFajGjz4/l206daiTN5TeSCdv3BKqTM3Atx/gf2ULLYQAhH1CDLiLvDvQvoPQeozp87374H0oagyAyWy/+kw/7QgTNWR1gu1NFVy15/YEZH3l5dZlRpDZbMSMnviAj5+dGzIpp/NYWXS4bFERDuCxqV7ITL1ecAF0g/mhoi4TxA6oR09VFU9K/PE1dTnwTU59AURiYh5C+G4Kvy5ybeAd43OubGIqsvOqmbmUk1Hpj4VqIewAAKin0eJuLm8TTtrMVQ2DYJY+NPSEIcPYLKY+PevLVzU64KgcWHrAlX/Bt9OEA6EuXCtEE9yNjp8AOHohXTPApkd/IL0QZ6Yfgi+7frj0qn1uxUJpWNkBUAoMYiEsUj/MU3y2Vz/zGxYc5ZgOP1KSESMQ/8FSVgtHiFMYGms+1pFQ6rp4PoD6T+MsLQG26Vls3K2dgHrZeCZH9igNWs/MUMQSlz+55pqg0+naEpYQJR/i8yyQJiSwJRU3mZUegynXwm5ZmB3lk5dFZJHb4u00eziJiHHSym1TB3PMlDiwN4ryKmtX7CRH0b8xpHdx2jV5XxufbEvVeuWz5dbejciT9yhhaFwIkUEmM6BKt8gRJibXTERQkDcKPAsRbr+BCUS4bgOYQ4tVgs5N+oJZOrjBO8JOCDyHoSwlKqdBga5MWL6lZTv3v6Fb9/8BZPFjAAsdgvvzBoSksEjpV9zTu7FaFkqNhACEf8lwnohc79ZyAcPjsnRfDGZTdijbHy2esRpry+QUiKP99A2RYOwQdRDpdYycPfGfUx4+Qe2LN9O1bpJ9B9yPe2valPkeVTnNMgYDmqyZqOptlaoJSIg4nZE5ACEqHjrMil9IF3a3oXRvb5CYrRLNNAl5Ugq/yzcRERsBBd0bY7ZEupgpHMqMn1oaP9VEY9MWMQN1R8I0b5RTArd+nfmuQmPlqX5IUjffuTxXui2QjTVR0maXeJr7Nqwh8c6DcGd7c6RLrZF2Hj043u4ckDRi8GklEj/QUjuCzKdU1W7drD3QIl7r8Q2lxZSepAZ70L2j4APlKqImFcQ9jNXivpsJT+nf3busBkUivhqcXS5qRMX9myt6/ABZPYvOg23ATyc2L8Yn07nLdWvsm7+v6VsbSHIN25fOn/qXw35AXe2K0ir3p3tZsyz/ytWFzEhhJYBJJ2E6PK4ZiF9+0tudCkh018NOHwX4AP1IDL1caRnbfkaZlAkDKdvUGwiYhz4ffqdtxKqF770P+14Or9/MYefR05jz6Z9xbZHmGqAqS6hCmx2cNxY7Hlzs2X5Nl1Nf4/TQ8rh1OJN6lmD7tOJsIBvW/HmLGWkmgbOqYTWJbiQmZ+Wh0kGxcRw+gb5IiKu19oLhmAhIqE9Ha5pk6N5cxJ7hI2bn+9TqPlXzFhL/3oP8cXTE/nqpe94+MIX+OzJ8cXu+iTiP9QKx0QkYNbi49YLEJF3FGu+vCTW0tfQkVISFV9MOQXzOWhVz3kn9Wtx/oqA/3Agx17vtV2n1xaDEmE4fYP8sV8N1ksAB9qfiwNEBCJ+NEKYeXb8I7Tr2QqLzUJEtANbhI3bX7mRzv0K7qTlynbz5s0jcTs9uLLdeD0+PE4P08f+ybr5/yKl5PDuoxzefTTfm4Db6eb3L+bw0lVv8e59M9m6+ytEzBuI6KcR8WMR8RMAC9L5K+rxq1GPdkJNfRrpK/pTRf8h12OLCE5rtTms9LjrMuwR+umuBSEi7tBxqIrm8M2NijVnqWOqE8iIyosClpan3RyD4mNs5BoUiJayuRY8y3NSNt0uBytnrMWV7aZtd+1Lf+JwKrUb1yy081sydSXD7/yY7PTQPYOO17Zl37ZDHN17HNCayAz98SkatqwXdJwr283jnQZzcOdhXFluhCKw2i08OPJurhnYPec4NWMkZE3klHicAiIKkfh7kXXdp346k68Gf4/P60eqKlfccSmPfnwvFmvxUy2lZ7WWJaUezTVqB9tliLgPEKL812dqxoeQ9RVBAnzCgajyC8J8brnZZRCKUZFrUCKEEGBto/0A6xduZGjv4Qi0G4LP52fAG7cUWejL6/bideuLca2YuU5rkBJg/9aDPH3ZK3y75zNSjqQy5+uFZKZk4vdLDuw4lJMyKlWJO9vD509NpNttl+CIcmjFWllfEaz9ooJ0IrPGIWIGF2irlBLpWYIvZRSXddvGuQ0E346qjtvXgVte6Fsihw+AuQmoeaWqXeBZhHT+qqVIIsHevdyaj4iox5BKNcgeo1XWWloiop83HP4ZhuH0DYqE2+nm5euGhzRIn/jKj7Ts0owm7QouTDrJjHHzQlr8gdaEXSgiyOmDpgw65tmvmfO/Rfh9p5qgSzX0adVkVti8fAdturVAuv8m2OGfxAuelYWyVaa/hnR+j0lKomLhvAvg5bG7mPldOo91PMDXO0fjiLQXPFE4PMsC6pt5L5wN6UOQBDRrMkYgo19Eibyt+NcqJkIIROQtEFk2SqkGp4fT/swohLhJCLFLCLFDCHHP6b6+QclYNUu/z63X5WX2hPmFnmfn+t38+5d+79a46rF4dJ4AXFluZo6fj8fpybkh6Dn8k+M5DdtdU8MbohSsIyS9G8H5EwJJ7lokixV63ppMyw6H2Ll0mCZMpxv3LgT5FmGpaNk9Lu2/GcMqVCqnwZnFaV3pCyGigfeBDoAfWCeEmCalPHY67TAoPm6nR3dTVVWlrohbOLat2hm2mrNOk1qkH88MkYkoLEII4qrG0LhtQ23Am09D9oi+Bc4nXfPQawgDYDbDsx/uQFF2IVOnaplDVb4vegjG2rEIB6vgngXme4t2DQMDTv9KvyewUEp5QEp5GJgHdCvgHIMCOLr3GK/f+B7XRt9Ov8QBjHnuf7idxXOYXo+X4weS8Xr0nVybK1qEhF1AE2rrfH2HQl+nWv2qCCXU6VvtFlpd1ozo+Ejd8/SuDSAUQWSMA0e0naQ6VXh7+uBTNxUlXM2AGZFPT4CcuYWdcF8VoYDVJjFbfFrjc/UwMvWZAucMvYYNEfeplmJKBGBH6zild10JUr8+wsCgIE53TL8OkFsYZT9QI/cBQoiBwECAunXrnj7LzhCSD6XgdXupVi8JIQSZqVk8cuELpCdn5Ky2p4yewY61uxgx5+VCzyul5Lu3f+XH4b+h+lWESeGmZ3pz+9AbglbkcUmx3D/iDsY+/w1ejw/Vr2KPstP2ihZcdNUF+VwhmNaXNyOhejyHnUeCCrzMFjO97unK4p+XcfzAidATBSFxb3ukjecnDsJsNROdEEXTDo2C5JxF5P3ItFcIbvtYhFaF9l6Q+SF59wWkhNCHFT941yLVdIRStN63wtYBkv4C91yQWUhTfUh5kNDCLVNQg5bKhlRPgHsZKJFg7WjINBeR0+30rQTXmqtoYZ4cpJRjgDGgpWyePtMqNkf2HOONm0fy3/o9KIogrlosL/zvMbau2I4zy4WaK7btcXnZtHQbO9fv5pxW9Qs1/2+jZ/DDsMm4coVUfhwxBXuUjRufCs7K6fNoL1peej4zx8/Dmemic78OtOvZKl/d/LTj6ayYvhahCNpf3Ybo+CjeX/Aa79zxEf/+tQUB1Dy3Os9NfJT4anHUPKcaO9ftDgklmS0mLVNGgOqXSFWl98M9ubjvReHFv+zXgW8PZI3V8uGlV3MWMW8V6rMR5trImNchfSgSb85NR1XBFFb5Qb8xfIHXUqLA0Yf92w7y+xezOfJfZy7s/A9d+yVjtQe+KsKGdC8GU70Kkcp5OlGzvoKMkbnqGiyQMA5hOXs6spU1pzVPXwhxJ3CZlPKewO/fAL9IKXXaDxl5+ifx+/3cec6jHN+fHOTcHVF2LujWnCVTQj8jR5SdQZ/cR/c7uhTqGjfXvJ8TOjICsYnR/Hz0q2LbDjBrwnw+fGgMIPB5fUhV0rzzeTw/cRDV61clKy0Ln9dPbOKplfHWlTt4+vJXclIxAcxWMy0vPZ/XfnuOZdNWkZ3u5IJuLajRsHBqnlLN1BrBmKoFxdyl/yjIDDDVz1d3X6opSNd8UA+BpRO4poHzR0Li/aZGKEl/FMomPZb9vpo3bx6Jz+vH7/NTt4nko983Yo/w53qycEDETSiFSDc9W5CedcgTdxIiBSHiEFX/NiSpc1GR8vRnAcOEEFXRgpWdgAdOsw1nHGvmbiAjJTPI4QP4vH5cmW6sdgseV7DjkVJSu3HNQl8j9VjeHHGNtOQMpJTFltA9vPsoHz38ZUhq5r+Lt/DgBc/y5YaRJNUOljZYOWsdXzw9McjhA/h9/px9gMtuvrjItgglCqytcuZKPfofsdZXUHz/BLJnbBD7FiJM6EQo8WDrAnhBqQaWBkjPYlCPBbpn2UGYEXHvFtm2k/h9fkbcPRq389R7v27AASxWf55QkhOyf0BGPVJww5azBOmchK5GEV6tcNB2yek26YzktD4bSimPAIOBpcDfwNNSyqz8zzJIPnAC1R/6ROZ1e4lNignRvjFbzdQ5rxbnXVT4opm659XSHa/duGaJNNMXTlqK36+/6ejMdDFpxJSgsXXz/+W1699lz6bQlESpSv5ZtImPHvmy2PYA/PrRH1yfNIATm29Ada4CPJrTlinI1KeQ3q2h1/YfQE2+CXmsC/JYd02337dHq+iNeRUct0DUE4ikuQjL+UWyR6onUFOfQz3Smv8WdsLnCf5KNG2bhVlvESus4NtdpGud0aiZhBYyoG2uGG6k0Jz2gKCUcoKU8pzAj25Yp7KTmZrFV4O/Y8B5j/Fwu+dJPpSCnrSjI8rOhb0uYNTiN2jaoTGKIjBbTFzS9yKGzx4a4qyzM5ykHEnVTbl8cORd2CKCN8RsEVYeGnl3id6Lx+1B9eln3Kh+lfULNwaNfTX4+5AVfm68Li/zv/+b9+//jKcue5nxQ74n5Uhqoe2Z+80ivnrpe6rVOkHN+m4dZ+pBZn8dNCKlD5l8K3j/QdvMdYN/DzLlblAzEY4+KLGvo0TdAyJKC0N4txQoGudxezl+4AjewzeC63eQ2dgjskI+r33b7eiqNksPmAr/NHfGE/Zm6gRrwVpPBhpGRW4Fw5nl4pELn+fY/uSckMi+rQeIrx5HypHUHIdosZmpUiuBLjd2xGq38tGSt/B6vCiKgskcHJdOP5HBuwM+ZeXMtQgE8dVjeG7CIFpf3jznmLbdWzFsxhDGD/2BvZv3U+e8Wtz92s20uqxZid5Px2vb8cOwySHhJwAEIfH4fVsPFDinz+Nj5rh5AGxYtJmfR07jk1XDqX9+wYVW37zxM+5sNwnVvPqOFBX8eZ4yPH9pMX/yPLFIH9L5KyLqfu1X11xk2vNoq1E/KEkQ/0VI+0RVVZn46iR+HfU7UvVhMsdy2xMubnjwGLUaeKjRwM2erXakqt20f/ykKh16pmFy5LmJWC9CmKoW+J7PGrz6xXxgAjVD04UyKJDKtfV/BjD364UkH0oNioG7stwkHzzBjc/0pm7TWlRvUJV+T1zN6GVvY7WfWp1brJYQhw/wUq+3WTljLX6vH5/Xx7F9J3j2itdYOi14A7hF56aMXPAaPx8Zx6iFr5fY4QOc27oB1zzQXTcn3+awcvNz1wWN1WpUI+S4gvC4vDxxyVBUteDc9eSDWhrojg0OrFa9lbgNrJ2Ch/yHwyhMusGvKXVK3y5k6lPazUFmak1R/PuQJ+7U2gvm4vthk/ll5O+4sty4nX6yM0z8771qzPpBqyd4bfxukmp6cUSZcETBvh02Zn0fn+dhT4FY/a5a0r0M9Xhf1MPNUA+3RD1+M6qnHJralDZ5b8YnEQ5QD59eW85gDKdfwVjz5wbdSlSL1UK9prUZt/ED/rfzE+4bdjuRsfoFTLn575897N64F3/eEIuEN256PyhFszh4PV6WTlvFrAnzOfTfEd1jHho1gHdmDqHmOdVQTApWu4XYxBieHf8o53cMbsR+z5u3YnPo510rpvB/rlmpWaycua5AexsGUlhTj1uYOj4RZ1buOS2gxCEibg0+ydKS0MYsaBLT1rYAyOxJhKZpSm2vwLP01IiU/PTe1JB/Y7fTxLejtIyi6nU9TFy+l9d/vpInPrmGrxbv4rp7TuTayLVBRH8UU2jRmXQvQKbcB76NaFlFLvCthRP9UNNH5fPJnAFYL0S/74AbzI1PuzlnKkZ4p4JRrV4SJosppPJUSkmVmglFnu/w7qMhWT8n8ftUxjzzNatmr+PYvmRqnlud+4ffQYdr2hZq7v/+2cOzV7yGz+1DVVVUv8pV91/Bwx8MCNlPaHNFSyZuH01WWhYZKVkk1amCSSfJvc0VLRny41N8/vREDmw/RHRCFPWb18Hj9FDz3OrM/+Fv3b08gMW/LCuwQfnAEXfwfI/XcWd7+PKNGuz410G/gcep3SiKiMRrEFEDQ4qqhOV8pK0DuJdyKl3QomXw2Htpv6pH0c3Nlyqop4rMvB4f2RmhUtIAKUdPfh1NKOY4WnXvjxAWVGdNyHhTu4awgaM/IvrJ4MsENjNl2lvoi8sB2Z+j2jqg2Ioi+VBxEJH3BBRHMzhV3uOAiDsQSmx5mnZGYTj9CsY1D/Zg2uezQ5y+qkqaFCEb5yTntq6Pz6tfKCRVlenj/sy51t7NB3jz5pEMnfQU7a/O3/FLKRly7TDSj2cEjc/8ah6tL2/OxX2C5Q28Hi/H958grmoM1evnH4fucE1bOlzTVjdVdO3cDWHTS0OeZnRo1qkJ7/75KuOHfMd//+zl4IFGZMibiGqYfzWxiBuNzJqo5eVLLziuQkQ+lFMNKmxdkK4/gew8ZzqR7kVgvxohzFisZqrWS6DVRdvp1T8Zs0Uy96d4pn9ThQZNXYAJbJdqTWACeeeKozvSfoX21CDsIbUEavZvkDkC1FTyLwqTkPkxnKlO31QVEicjMz8C99/aU1nkvWDvU96mnVEYTr+CUbtRDRq2qMeWFduDxqWqMmnEb9w+tGi9XqvWTeKCy5uzZu6GkNekDNWycTs9jH3xuwKd/vY1/5GZEpom58rSuljldvqTP57OhKE/oPq1p4Erbr+UR0cX3HREL1W07xNXMX7wD7rHd7mpk+54Xpq2b8SIOa8U6thTtlgQUfdB1H36B9ivhKzx4NtCniJzcM1BKjURMU8jhOD9KZlERRzAEaHtQdRt5KJLn3TUqHGIai0QOoqbQohAC8hgpGsOpL9MaO/aMPj2omZ8AL7NYG6OiLgVYUos3LkVAGGqhYgdXt5mnNEYMf0KhivbzY61/4WMe1xe/hgzt0hz+X1+pJS8+ceLVG9QVTcsrcfBHYcKPMbj8upuzgK4sk45oEU/L2Xci9+Rne7EleXG4/Ly57eL+ezJCYUzJg/XPXwlkXERIeM1zqnGRb0Kr/1TWKSajvTtQEr9kMxJhLAiqnwfpp+wC5zfaI1YvJtISvwnx+ED2CMk57Xx0bJDqq7Dz9e+zI8otMMHkCc0OQr3fMgajTx2CWrGJ8WXhDY44zCcfgXD7/WFC1kHVWnmx/I/VnNX40H0st1C3yp3M2nEVL7c8D497768UIVW1eolFXhM4zDNUhRFofnF5+X8/s2bv+hsWnqYNWFBsZRAI2MjGb38HVpd1gxFEZjMJi69sSOjlw3LV/unqEjpRU0bjDzaCZl8I/JIe9SMj/LNvRfCrm0q6k6YDfjBs4qQ1E/ApLiQnmVFN9RfcIprLgvRwj8n/46kZkvWx8jUx4p+7TBI6UL69iNl4f5eDU4vRninmKiqypq5G1g//1/iq8dx+a2XEF+1ZJtJa+b+w+jHxunKB5vMpkJtsK5fuJE3bh6Zk8+flZrN98Mm48pycWxfcoEFQ7YIKwPevDXfYwAsVsmzH2fy9kCB1y04+RihqiqTP5rOBVe0pE23FiTrKWWi5dpvWbGDVl2KnhZau1EN3pv3ao4sQ37OXvp2ILPGgncbWFogIu9FmAtWb5UZ74FzGlq1bsB5ZY1DKtUQkTeHP9HSXOsnnBdTQ4QwI5UqAdG3vA7RBkroXof0/QeuuSBMYOuBMOepRTA30r9eyPVbgj80xKehgnsx0ruhRMJlUqra55b9TUB+VCAjH0BEPliiqm6D0sVY6RcDn9fHCz3e4LUb3uOH4b8x7qXvuPOcR0KqS4vClhXbebnPcPZtORjymi3CSmxSDPcO61/gPBNfmRRS0erOdjP5oxmkHk/TPynwfUyqU4UnxzxI5+s74HF5mDVhPm/cPJLPnpoQWjTlmkGn7jup38RF3riR2+lh9KBxADTt0EhHflirxh189bCwn9nxgycYP+R7Bl/9NhNf/VG36tZkNuXv8D2rkcevB+dv4PsXnD8hk69DereEPQe0Clyyvyc0bOKE7C/yPVdEvwQ4OPXVEoAdETNU+9XeDd21llAQjuCaBTXzU+Tx65CZHyAzRiKPX4Wa9W2e6z2Dpr0fDjtYL4Yqk9BEbsOhgmd1Pq8XjMz6BLK/BVxanYLMhszPkdn6ezAG5YPh9IvBjHHz2LRsO65MzSl4nB5cWW7evHkkfv0yT0Crtv140Fh6x95BL9utvHT12xwIxM+1StHQx2GhCO545Ua+2vwBVWqEawZyiv3bQm8aoLmeNt1aYLWHbp5GRDmYmvE/vtvzOd1u64wz08kjF73A6EHjWPTTUqaMnslDbZ5jyZRT/WSlez7gZPcWfYezf+tBfF4f97x1G7YI/WPc2W4+eHBMyPiuDXu49/wn+On9qayYsZZJI6YwoOnjharWzY1MfxVNQ/9kOEVrdCIzCpBUli7CZsGo+k8uJxHWVogqk8DWA0z1wHY5osq3CJu2ySyEHZHwNSi1tfi/iASlCiL+y6ANVendDpmfowmM+dBy7t2Q8Q7SfzjX9S5EJIwDSyutAYtSH2w9wVQHTA0h6nFE/BfazdHRB60xi57hFlCKv6ErpdQ2ssm79+GErPxvlAanF8PpF4M5Xy/ULaByOz3sXLc77HlDrxnGzHHzcGa48Hl9rJq1jkEdXiI9OUNXYAzAZrfS/qq2RMaEbl7q0aB5GCkCRXDzc31IqpOYo7GjKAJbhJXHPrs/qKn3lE9mcXDHkZz2h36fH7fTw7v3fHIq/VOpgpQK0fH6NzlbhA2T2UTDlvX4aOlbmMz6f2qHdh4OyVv/6OGxZKc7c6qSPS4v2WlOPnms8BLPUnqRvm36L3oKCIeISN1QCxAo1CrgdEsTlPiPUJLmoMR/HhIyEZamiKQ/EQk/IxK+RST9HdLBS7pmot+iUYBrTvCI9UKUKj+hVFuHUnU2SvzHKEl/oiTNRIm691RaafSLYGkdxmpzCRuzeAP7Fjqox0swr0FpYzj9YhDOgSHDV43uWLeLLSt3BmnQSFXidnqYMe5PzmldX/c8V7abzJTMQtt21+u3hFS02iJs3PLcdcQlxfLZ6uHcO6w/bbu3pPudXfhg8Zt0u61z0PELf1qCxxX61OH3qexcrzU+y3Jfg8cF1z9wFJsjeGPSFmGl98M9cuK4DZrXJaG6/lOKYjIFPX2oqsrGpToql1KyfsGmQnwCsGnZNu5v+RzucAk3SnS+5wshEDEvExw2UQAHIvr5QtlQEEIIhKURwnJ+mEYo+cXAQ1+TahZq1gTU5P6oKYOQnpWhZykRKFW+h5jhIGLQ3p8DTHUQCf8LtIUsHkJYw4u/Wc7THzcoFwynXwx63dsNe6QtZDwyLoKGLevl/O71eJn03lQGnPcYL/R8U7dIyuP0sG31f/Qfcn3Y6/3vjZ8Lbdv5HRrz1vSXaNSmIWaLicRaCQx89w5uG6zN74hy0HfQVbwzayjPfPUI517QIGSOiCj9L7/q9+MIvPbHV3v46IX69LrtBD1uScZqU3FE+hFCckG3FiGbwX0fvypExdNqt9Ctf2fMllMxbiFEiFT0SfKer8fh3Ud5vscb7Nm4nxnfVsHlzOsgbaDUQT0xEDVzHFLN0J1H2LsiEsaDtbMWKrH1QFT5GWEpuR5RYRCOK9HPs5AhK3KpZiOTr9c6SnlXgnsW8sR9qFkTdedWIvoiqq5AVPkJkfgLInEuohCOWUpVu7EcvRT1SCvUE3cHyVCL6CGE7i/YEdEvFDi3wenDyN4pBt1u78zy6WtY/sca/D4/ZqsZk0nhtcnPBW0svtJnBP8s3JRvqqXVbuGc1vWpWicRs8WETydzZ9vKnUWyr1WXZny6KrSAJeVIKot/WY7X7aX9NW2pHUbcrPcjvdi2+r+c8A5ozrhavSRqN67Bwp+W8usHv5NyJJpF05rRuFU2tc9x8d8mB7ZIO5de3zHIkQP0e+Jq9m87xJyvF2K1W/C6vVzQrQWPfHRP0HFCCLrf2YU5ExcEPRVZ7RauvLdrge/9t9Ez8AXCQuPerEFCVS8deqTj9ShERguE8IFvA1r65DJk9gSo8hvCVCVkLmFtq8XLi4lUs7UN2mKsoIX5XGTUI5D5CdqeRCBDKnpwUNcvAOn8CfwHCd54dkLG+0jH9bp9gIVQwNIkZDw/ZMYwyJ5ETtzeswR54maoMgVhroewd4X4L7XaAf9uMDdBRD2OCDSuKW+k/zC4F6D1R+5WaaUbTmu7xKJS0dslblu9k38WbiKuaiwX970oKC6utft7VTf2fxIhBJGxEYzf+iGRsRH0TbhbdzO3btNajNv4QYlsXTDpb94d8CkCLYQihMJNz/XmrldD0w+llHz25AT+GDMHk0Xb+IuKi+TdP19h0ntTmfft4qAbQm5sETZGLXqdRm0a6r6eciSVvZsPUL1B1bD1AM4sF6/0Gc6mJdtydIhaXtaMV395JkdVNO14Ogt+XEJmShYXXNGCpu0bIYTgpaveChFeS6jmpcH58Or4/ViteSUczOC4FSV2aPgPr4hI3w5k2gvgDYSjrJ0QscMQpoLrH0Ln2q01SkcBe0+EKbTZjZp8O3hXhJ4sohBxHyNsRe8yFmKHmoY8egm6TdodfVBih5X4GmWJmjkOMj9Au3kqgAqxI1EcZ2eD+YrULvGsonHbc2jcVr9IacuKHcgwUr+KSSAltOxyPo99cj9xSdqK46r7rmD62LlBjt8WYaP/kBsKtGXXv3tJPphCozYNgnrNgqan/96AT/HkeeL46b2pdLimHU3yFFoJIXj4gwHc8NQ1/Pv3VuKrxdKyy/ks/HEJM8b+iQwj4Ga1W2hy4TlhHT5AfLU44qvF5fte7A4vw2c9zt4tGezdcpB659cO6uy15s8NvNJnOFKVeN1efnhnMu2vactL3z3O+Z2asH7BxqCnhBNHLMQl+rBY9DR7fAGnWjpOX6ppyORbAqJggc/J8xfy2OVIywWIyP5g61novHVhrg/m+wKr1L+RSiTYuiByV/6G1ZFXS09j3rc7UF+Q1+n7wRsu/79iIL2bIfNDQm5YaU8hbX+FCOyd7RhOv4xIrJWA2WIOaR5idVgZ8MYtXP/kNSFf/IHv3oHP52fWV/NRTALFpHDHKzfR9dbwvT9TjqYx+Kq32LvlIGaLCa/by/VPXsOAN2/NmX/5H2t0N5g9Li/zvlsc4vRPUrVuEl3raqvTeT/8xbv3fBrW4ZutZq55oAcD3iq4sCsc0n8QmfoseNcBUKdqI+o2epdjh218/vREtq7aSf1mdZj3XfCThivbzfI/VrP452Vc+2APJn84HZ/Hl6MuanNYufExu269AKApV5YS0vlboPAq9+ekAh7wLkemrgXlPaTlfISjL9guy/cGIKUfmTYUXFMCfXwVQED8GIS1HdL9N7gX6pypaBlI5qK1bgyLqZYmNKd3HZP+309FQZ4ssgtB0eQo8tRHnO0YTr+MuOiqC7BH2XFmuYIcpdlsovudXXS/6GaLmcdG38f9w28n9WgaibUSChQle+Omkfz3j6aXf9INTv5oOue0qp8jQBbOUSO1IqmC8Pv9fDJoHD6Pfu66xWrm1pf6csfLNxU4V4gJ0qWtYKUbMt4OpPcFbPJtxnfsZh7veB6pxyU+j49NS7bq2uzKcjNrwny63NSJT1cN58vnv2HVrHVERNt4cFgtLr5iZlhJZqwdimx3WHz/kb8WjgfUveDei/QsBHsvROw7ukdK7z/IE/eATD85cOq1lAeQiXMg9RF0m4UrNRDx4wr1RCHVNPDvAVNthKIv3y1MiZrSp2tunutZEVEPFHiNkiKlisycAK7vQfWA/QpE9CNh7Q0m7004gJBhbmRnN0b2ThlhsVr4YPEbNLqgARabBavdQq1G1Rnx5ysh4Ze8OCLt1GhQrUCHf/zgCbYs3x4iKezKcvPLB7/n/H7RVRfoyg5bHVYuv6XgeO/RPcf12x0GUMwKPe66vMB58iLdS5BHOyLTnoG05wOa9LkdusTvcdKh+6GcG05hblLV6iUx5IcnmXxiAt/8E80l3X5FyOQwR5tKVZpXnCySKgzSCc4ZSG9oVyspXcgTA045/NAjICtcg3gT2K8JlWwIuYaKmv4G8uglyBN3I49eipr6HDKMIxSxw8FxI1qGjglM9RDxnxW5EXxRkWoa8lgXyHpHuznJQ5qA3fE+YbOvguy290S3aln6wdal9A2u4Bgr/TKkRsNqfLJyOMmHUvB7fSTVSSxVDZKs1KyckE5e0pNP5fbHJcUy6JP7+PjRcTnyxhabmavv7xbSuUqP6IQo/L5w+xMKQyc9XSiRttxINROZ+nD4gp4AVrufGvUKFmazR9roOSBPdo/3n0C8PmzCPpjqIqylqM7puAoyPwqEePLTtj+JG9yLNc0eAjfCrC/At127KeSHzAbdRAyVk+9ZSp/W49d/FCytEZZTHaZk1jjI/lmz4WSs3jUTqSQgYl7Qnr7cC7X+s7YOAVnjl5Exg0G6EUohb24lRKYMBDVvVzYJ6glk9k+IqHt0z8vB0g4cvcE5Fe0pTAEsEP1ssTbXz3QMp38aKIx8QnGo3bgmZktoWb3ZaqbDtcHibFcO6Erry5uzcNJSPC4PHa9tp5ujr0dUXCTtr2rD8umrg3r3Wu0Wnh77UIHdqvLi82Yy89PBTB9fG58XuvZLoc99x7DqhNadWQpb1+q3hbRF2PC6vVjtFjpc247O17cPPsCzJLzqJWYwN0XEf6J7I5bSB87fkM6fAYlw3ACOvkHSx9J/CFzTtWvYLgsUWtmhyi/IzPfBNVPrl5svVhBaSqWa/Qukv074m1RuA70QcRs4fw19TdgRtm5I317kif6Bnr0qIJG2yxBxo7RGLNkTdK7lAuf3qPZekHIvWm8AFdJVZMSdKDHPaucW9mmmhEjfftB5EtLwBFpR5u/0hRAQ8zo4+iJds0HYEI5rEeaiNyU6GzBSNs9wFv28lBF3j8br8qKqEqvdQnRCFJ+vfTcnK6g0yM5w8tato1g7718sVjN+r5/bBvfjtpeCi8qk/7DWL9a/GywXIRy9g1aE0rOa168fwsp5EbidWnTRalepf142H/2+k+DiVAtpKbEM6FiPrPTgm83lt15CkwvPJeNEJm27t6TJhaFfYJn1DTJjOKExbytEPYwS9bDue5VSIlMfBPeSPOc6wNoeEXU/0ncI0gejxYr9gAUibkJEDw66iUjXPGTas1ooIaSrFoAdkTQflBjk0faFuEkACIh5CyXiBtTM0ZD5JdoKVmrO2HY5InYkMrlvoKlL7qc0B0Q/jxJ5G+rh5oRtrSgSNO39oDEHIu7TUkkBzYuUEryrtaczpZoWsxc2pGcdMuWu8E89EXegxJReuu3ZQn4pm4bTPwvYsXYXv374B4d3H6Vdz9a0vaIls8bPZ++WA7S4tCm9H76yxLLPJzl+IJnkgynUbVoLR1RwwxDpWYtMGQDypGa7A0wJiCq/IJQEpPSwY95lPNm7Ro7DP4k90s8Lo/fQsWe2JhEgTGDvhWp7hOEDvmbJbyuw2LSiLq2P7pPYHPln3Uj1BPLY5ToOw4GougCh6D+BSc8q5Im7CesQsaE5+rzhGwciYSzCemHwfNIL3k1a7D7zXU5tpamIuA/A2gWZ+gy4p+X7fk5hQlRdllNcJD3rAr1j3QjHVWC9VBPES30I3Q1McxOUxGmoyf21Ct68KLVApoIM7YyGpTUi4ZscPZ/SQEoPMuV+8K7XwmLCCtgRVb4DpSryaCf0N8dNiMTpCHPhnlgrE4bTr0SsX7CRwdcMw+fx4vepWGwWHFE2Pl01okhxd5/Xx7F9ycQkRhdK7E1KiTzeHfx787xiAcctKLFDke6/mTJqMF++loDHHRqW6nNvCg+NaIaI+yAk5HJ033H2bj5ArXOrU6NhtUK/D+n+C5kyCE28zI9W1foiSuQdYc9RMz6FrA8KfY1TiMB7fS28PdIFnkAhlfUihLCjpg+H7KJU/gqoMhklzAaq9KxDnrgD3aweAKUeStU5SO/GQPjHjfbZKIANogZB1qdhnjpMoCQiqkxCmPQruouKmjkGMkcT7NhF4OY0NfzrsSNRHFeXig1nG/k5fSN75yxCSsn7932GO9uds/HqdXvJTMniq8HfFXqe37+YzQ1V72Vgq6e5sdp9jLh7tK4AWxDqUfDn3WwD8IJ7dsBAD/FJfkw6SUlWm0piw6sC8ebQGHvVOom066GV8y+ctIRNS7cW2BBGm7htLiGwQHw6413U7B/Dn1OivP38N+qFsCNsl2o/wo5UMyF7fBGvIeHEnVq8W+/VjBGEdfgA6gHU9GFgboyo8ivYe4OpEdivRFT5ARFxWyAcpYcf1GPItNIRngPA+QuhK3kJvl1I/2GUqIGIuPfB0kaTpLZfB4kLDIdfTIyN3DJCVVUWTlrKnK8XIISg54DLuaRf+1Jt6ZeX9OQMju0PTU1UVcmqWesLNcey31fz+dNfB8lHLPxpCUIRPPvVI+FPFFb02gBqrwXS5awX0v6KdKzWRFxCIuUpB6mYrXQfMCCM4qT2eb53z6csnLQEs8WMKiVV6yby7tyXQxQ8pZT8/sVsvnnjFy69ahv3DD6MzX7SNhVwQfrbSPu1IRkoUsoSbFLaEY7eRTvFvaiAA6IAnRW3zNBCQnHvIcy1g1/zbS5gTq1JjFTTUOLeQcSF6jTJ2DchbTD6YRUVPCuR0lUiZc5T5NefV/t3E/buCHv3UriWQYk8kBCivRBilRCidq6x5kKI9UKIPUKIj0XgWyyE6CKE2CaE2CWEGFxSw0sLKSXHD54gKz3/1MGizvnmzaMYef9nrJy5jhUz1vLugE8YftfoUruGHraI8CvUiBi9ht2hfPtWaE9bj9PLvO//CtG9z41Q4lFNrZEyb9jGDo5bA8dEYU16g3d/3Uethh5sDhV7hEqV6oK3/hgcVn4Z4Pcv5rDo52Watn6GE1emiwPbDvL2bR+GHPvLqN/5/OmvOXEohYuvSsvl8HMbbNI2DfMgM96EDP1iqfAo2vuM6I+wFi2TSYu5h2lsghkSvkW/45UE31rk8V6o6e/mea0w8XYXuP5Aqim6ryqOaxGJUwqYq5RCw+EqepVqoJROCMngFMVe6QshRgCtgXp5XvoUeAGYDcwDegshpgBjgeuBncBaIcQfUsp1xb1+abBq9npG3vcZacfTUVXJRb0u4NnxjxAVp58iWFg2LtnKyplrg6UCstz8PXkF21bvpGHLeqxfsBF3toeWXc4v8fVOYo+w0bF3O5ZNWxWUWmmLsNJnUK9CzXFsn34Rk9/r58ThFCKitZvHns37mfbZLI7uPU6jNuewYfEm1i/woijNuOTqDB55+xgxcT5NJybyzpx5lIje1O/UirGrf+HgjmP4RHvqtrwGkyn0T1FKmRPqmTJ6RsjNyO9T2bR0K2nH03MK3vx+PxNfnZSjM5SeYkJVIfQBSw3R1Ze+/ZD9I+E3cPWwaNky0U8ULwXQ1pmwa6/IhxHmmvm4Vgm4tUIlW6dcWTWF/FoLi6bOGWZDW5gbIB39AuGX3LUgCljaBOv/FBMp3eBdrv+itY3RW7cMKEl4520pZaoQYvfJASFEEtBASjkj8Pu3wJXAfuCIlPKfwPjPgfF1eScVQgwEBgLUrVtwA+visnvjPl7t926QI1kxYy1De7/DqEVvlGjutX9u0FXL9Hl8zBo/nwU//o3P4wcBPo+fhz+8m6vvL51H16e/fJAh177D9jX/5Wj/XH7LJfR97KpCnV+vWW2SD4a2BJSqZOe63dRuVJOl01bx1q2j8Hl8+H0qS6ee2mxX/YK//ohj19Y6fLH2OUzWxiFzCXM9TDFPUSfMoli6l2orbt92JDFg7YAz/RB6zlEoCq4sN7GBTn+ZKVk5bSwBpk1IpN1lGdgjcrtOAUpSqC6Nd02YpuWA5ZKAkmXe18yI2DcRxRQ2E0oMMnYYpL2Ilg3kB0xg64WIegQhBNJ2WUASOMzNSDqR2T+ecvpKHPiPFXxx6QVT/t8xEf000rNCK46SWVroSzgQpaWq6dtB2H2QsPn5BiWh2OEdKWWqznBtIHf6xn6gBlAH2KMzrjfvGCllOyllu6SksquW+2XU7yGVrD6Pj+1r/mPvlsL1Yk0+lMK87xaz7PfVeD2n5oqOj8JiC72fmq0mZk2YT3pyJtkZTrLTnXhcHj57YgL//bMn5PjiEBkbyahFb/DJyuG89N0TfL3jY54e+1Ch9xKa5VOhu37hJvw+P+/d8ynubE/YKl2fV+XofjfrFxdcSZsX6VmPTHlAq0gFIB08s+l0ZTJmS+j14pJiqFr3VG/XtOPBsgXr/ormm5HVcLsEWelKoI9sTUT82NBVpBKqqa9hBksTRPxorZWiiMr5EfGfFtvhA0jpBM8atK+i1Prqxo1GiR+ZY5+IfQdsF5PvGs2749TGdsRN5N8sHcABEbchCuoipsQiEqdpNkQ+goh5BZE0D2EupQWZkhBI8dWhElbLng4K9ARCiC8CcfvcP+G6IuTdzVPRli7hxsuNA9sP6eq4mCxmju4tuKfnt2/9wh0NH+GDh8YwrP+H3Fzjfrau3AHAZbd00nWyql/VVbv0enzMGPdnMd5FeOo1rc1FvS4gsVY4R6ZPw1b1dLuCWWwWEmslsHfzfl3Zh7z4fSr7CnnzzI3MeBe9zcP+TxwhPsmHzaH92ZgtWjjr2fGPBDlvRVFCqpR/+rQa/ducz+evt0TEjw84rbxRSQLCa3qhNjMi4maE7TItPz7uI02nvuqyEhcqyRMDwfkTOQ3c/fsg7YWgWLtQolDiv4AqM9BqBHRQ9yKzf9COj7gNbB20xutYtRsVMWBuoZ2vVIPoJwrd+lEIC8LeEyX6cYSjb9iwjlRPoKY+i3q4JerhFqipTyL9+X+XhKlGoO9w3huaAxFZgLyCQbEoMLwjpSyKhN4hIHeXh9rAvnzGy42WlzVjy4odIQ7M6/JyTisdh5CLDYs38/2wyXjd3qDzX7r6bSYd/JK4pFhenfwsb948CjWgqW8ym7j2oR5M/mh6yHyqXyUzRacQphy46Ko22Bw23NnuIFkXxaTQ467LAiGdgu/XJpNC/Wb5C37lRXX+od8MBIit4mfMgq3M/jGe9UuiqFnfSu+nR4fk7NdqVIPE2lU4vOto0LjLaadh29sQ1gvwuL38PXkFezfvp975tbm470WYLRKZ/qZWlIRAi5dbNUmD2BGarj0ghA1s4aWui4L0btIKkoLSK1WQLmT2pBD1SsVSDzV2GKQ9pTObB7JGI00JyGxNOoKIAeBPAXNtiLgTRbEh/QfBsxKUWE7l5pfCe5E+ZPKt2k3rZNGaaxbSuw4SZ+VbzCXiP0amPKKFc4RFSxeNfgJRCcXQTgelmrIppdwrhMgSQlwGLAbuAAYDK4AmQogmaKGdfkDhdhbLiOseuZJpn83C7/PnrPhtETZ6DriswCYff3w5F48zNHThc/tYv3ATbbq1oG33Vkw6/CWblm5DCMH5HRuTnpzBT++HVl3ao+xc3Pei0nhbRcbv8zP1s1n88cUcvG4fF/e5kCvuvDTnd7PVhCPKwUvfPU5iTU3Gtt75tdmxbnd4yWY059uyS+HVF6WaDWkv5XtMRJRKn3uT6XNvMlg7oCSEFmkJIRg66Sme7fYaql/Fle3GHmmncZuGXPvwlSQfSuGxji+RcSITZ6YLR5SdsS98y5dLo7Axk+C4uYDYEVobwLLAt1PLIgr5GN3g3Rg0In27wX8EYbsEebLzU17UY1o/gpNPSp5FaJlBVsj6DlWxg/+/U2PCDgkTEUVsm6iLe2FAJTV3qMYHaqomx+wIv6cklAREle+Rvr2gJmv1A0rpJDcYhFIWefp3AROBOGCClPIvACHEvcA0tFDPcCll6QSxi0l81Vg+Wz2CiS//yMqZ64iKj6Df49dw9cCC26c5M5y64oYSGbQxbLFaaNXlVCPthOrx3DH0Br596xc8Tg9SauqQ53doTMfeusVzZc6bt4xi5cy1ORvPP70/DSE08UbFpKCqKk+Pe5gLurbIOefVX5/l2Ste5/Cuo7ohsrpNa/HevFeKlnnhXRXGAeqhgC38v1Pjtufw3Z7PWDhpKcmHUmh28Xlc0LU5Qgg+fnQsyQdP5OxHODNdKIoLxTsfLHmfYNyQ9TmUldM3N0S/tsEGlqYASDUVmfKQdhM42blKRINM0zlPEBoa8wNOkAdyBVR92o/M1hQsk+aHrY/IjVTTA7r7tUJ17H07QOrk9MsspG97ASVrAevNdYGyS94w0Cix05dS1s/z+xqghc5xM4HQVI5ypGqdRJ4dn0/BURi63NSJNXP/CekT68xwhW2ReJJbX+xHi0vPZ8bYP8nOyKbLjZ3ofH0HTKZwudplx3//7Aly+Cc5eUM7KcM88r5P+eHAmJx9iqp1k5iw9SOWT1/DqIGfk53uxO9TMVtM1G5Sk/fmvZqT2ll4rIT3+CaCt4BUyHgPFYkSeZfuGZGxkVx1f/CNQUrJsmmrQzago2Ld+L0Si177Av/Bwr6BIiMszZDmpoEslZP/BkJTgYzQehfL1KcC9QTeU05VAlgITqM0o91Aipg7L9PA928grh7mEKlqwnXZ3+VkN0n7lYjYt0+FbcwNtCeHvHo9IgJhrtidtSobRkVuMehyY0d+em8qO9buCnntnTs+5qcjY/MVA2t+8Xk0v/i8sjSxUGxZvr3gg9BWw/u3HQrqUyuEoMPVbflu7+esmfMPB7YfpkGLurTscn7xcqut7dAcv97ehoSQkIYLMt5DOvoVmIGSG6GE2pZ82IJfd5tCgCVczkLpIOLHIjOGBbTevWC9EBHzqiZQp54Az3KCnTuAB5S6oNjBtxfMdTTNeNdv4dUowyEFMutbpO9lLZPGehHILIRSBezXIkxVkNlfQ/YPBOvuz0YqcYiYIdrvtsu1fH+/m1MhHpP2VGLvUcxPx6AsMLR3ioHJbKJBy7q66cVCEayZW7EbRZ+kSs34Qj1hqH6ViGj9FECTycSFV15An0G9aHVZs2IX0whhRsSPQT9nW0U3DCIs4F1bhGsILryydci432/i7zkXB7Jdco7W8tGjHkf6j6JmfoGa9hrSNUvT2i8lhBKFEvsWoto/iGqbUBL+ByISNeM9ZMqDhG3EIr0oib+jVP8HJfEPRPSTxbQgG1x/gG+T1mwlcyRkfYHMeB95vCvSvQyyvkJXdz97EjKg0SOEBZEwCWxd0daSJq3HQJWfSlWR06DkGCv9YuL3+sM+Sfu85ZqNWmja9WxNRIwjpI+vHot+XsZ1j1yJyVx2YShhbYVUEkEtRGER4Ha6cXolCbULPjbnnGyd2gEp8So3I2JvQmZ+qgnHWVshop4CmYY8flOOXLR0TQZTA6jyXalUpJ5Eu1kKpG8HMvmmQIFYPpXBpuBNbKHEQdwYZOojgF9fFjn0qmihM73ruEGCTH08n6cHL+BBqn5wzQaZgoh6COI+zvWeDCoaxkq/mHS99RLdfHaf10+bK0K2NCoE+7cf4v17P2Vgq6d5u/+H7Nm0n5ELX+fcCxpgsVuwOiw4ouyYreagL6zH5eWrwd/z1q0flIodh3cf5eNHx/JQ2+d4u/+H7Fy/+9SLti6E16I5heqHE0cFD7SdSEZKYRqPaIJ0GxaHipFJCdO/nKPloidOQam2TMuLNzdCpj6J1tIx4BhlNvh2ILO+LtQ1g66jpiOdfyBdMzR1Tb1j0t8IOOwCpCB0lECFrT2i6lJE/BcQ8z75fo6mxmA+j4JbOnognLyEqTZ4tyKPXYpMfx2ZMRKZfBsy7WlKTZfHoNQxVvrF5KKr2tCxdzuWTl2FO9uNyWJCMZl46ssHC6U/r4fPq30BzRZzzu9/fruYQ7uO0Pn6DpzTsn6x7d2xbhdPXvoyHqcH1a+yZ+M+lkxZyVt/vMinK4dz/EAyXreP6g2q8tP705gw9Psg/R53tpvl09ewZ9M+6p1ftPz73OzdcoBBHV4MVPT62bl+N0umrOD1356nzRUtEVGPIV3zAo7Pjea4LGS4umOVv+P1ChQF0pLNvHRbQ7LSXLx58yiklNQ6tzp9H7+auufVQlU1eYj5P/yN1W7hynu6klS7CiazKeh9nSTtuE6Dbf9/YTTl3VoMPqrwJSyq83ctJVUEHLH0I2PfR3Hkkd/wrKJgh2kK64iFsGg6/VZQ3XPBPY/gOgArxH2AYr8CNeWxQGet/K4nIXIApA/NpbsvADtEv4xMfSjPZ+QF959ayMhxbQHvw6A8MJx+MRFC8OI3j/PvX1tYOm0VETEOuvXvTI0GhW/wcZLjB5IZ9cAXrJq9HiS0uqwZvR/uyVu3jMoJFX37xi8k1IjjnVlDaNA8/+IxPT5/amKQJo2qaumlHz8ylrH/jgqq3N21YY+uY1QUwZYVO0rk9Mc89z8tyymQIiRViTvbwwcPjmHi9o8RpuqQNB2Z9a22iWmuh4i4m7W/HuWLpw5Rr3EymWlmtq5zoDkfL2vmamqZ6xdsZM7/FvHKL8/wxxdzWD1nPa4sN0Jo4al+T1yFLcIWknVlMpto17O1jrVWtN6yOhQyTi2lB+ldD2kvAJ5g/5r2NNI6H2HKVTUtHJomTr5YEZG3F3htETcCmf5aYJMYbaM1+mUUu5bVJCLvQroXkm9PXhGFsF8NlubIrM/BswHM52hhHOnVmsKEvGmn1rDccPoVEsPplwAhBC06N6VF56bFnsPn9fH4xUM4fuBETs77unkbWPtn6GbwiUOpPNTmOZ4e9zDd7yhateLmZdt0x/duOYDX48ViPZWvWPOc6lhs5hDHLxRBUp3EvFMUiQ2LNuk2Pzm67zjZ6dlExkZqxTrRg4BBOa/XOEchM11h9cKYsHOrfhV3tpvhd3yE2+nJce5Sak8qP4/8nVaXNWPVzHVB5zmibNzx8o0h8wlzXaSpVqCgKbfNDnDcEnK8VDORzing+w9hbY5U0yDzo8AKWc+RS6TzF0TUwFxT3wzZX5NvE5TYNwuVBimEDRH7NjLmlYBYWnxQ2E5Y2yJjhkDGW4ASCGOdlHq2glAQcZ9oOfzmBojYYN196VmXz9XPjH2tyojh9MuZpdNWk5GSGVTklF9DKL9P5cOHvuSSvheF9KjNj6i4SE4cTg0Zt9gspCdnUKXGqWKbXvd2ZdJ7U4OcvmJSiE2MofXlzULmKAzOTCeHdx8jKi6S7PTQlaViUrA6wq+eG7dtSN2mtdi5fo+2iZ4PGSlZukVjSFg3L1S50Ww1k1AjTncuET8amdwfbWPTBwhNLjriBm1KNRPpnAqedeCZrV1EOpFOK5qjzy904obMUaiuaYi4UVqM3HKelvPu2xrm3Kh82xRK33/IrDHg3QyW8xCRAwGr1qJRiUXaLg3KplEibkQ6rgXvVk2aQWYGjo0HW4/8K2MtzdHqBfLiQDj65fO+DcoTw+mXMwe2H8LjLFjALDcms8K/f23hwisvKPQ5/Z64mv+9/nNw9ooAr8fLHQ0fpX7zOgz+/gk8Tg+fPDE+JMul7nm1eGv6SzkFWtvX/MeKGWtxRNq59KaOORINeZFSMmHoD/wy6ndMZhOubDcmsxJUIGW1W+h6W2fMcjVq+iJQ4hCO3lqo56SpQvDOrKG8ecso1swJbX6S95omsylEI8jv96PqKIO6nR42L91G80tCn9iE+RyouiggM3BM05G3aDUWqncrJF+P/qZrYTX5/eDbpmUICUX7XZ6Mm+s5fY+mxKmDplB616nYu2+rdkNC0dJbEYAFEiYgcvXXFcIO1lz1CJbmhbJcCDPEfYhMfTAQBnMDEVrNheO6Qs2h+z7ULM1u7xowN0Q4bkSYSvaEaXAKw+mXMw1a1MVqt+DMLPzjsFQlJkvRUidvfKY3R/cdZ+a4eZitZm21LUH6JV6/lx1rd/FYx5dwZbt1b0IHth/iy+f+R92mtdm//SB/T16J1+3FbDHx1eDveP7rQXS+vkPIeVM/nckvH/yB23nKCSpmBcWk4Iiy43V7aXdlax5+/R9k6heBEIMVmTka4kYh7N1yzouOj+KNKc9zfdI9IXH5k5gtJpq2b8TW1f+FCsOFWXQLIXDp9D849boV9Fr1nbidojVcCYcEsvJ/xAPArvWxDSM5LDPeCHx+Jzl5g/MH7RPIlAcgaWGhpBcKQtg6QuI8cP2OVJMR1o5g7VDsdE3pP45M7gdqGtpegw2Z9SUkfBN0ozIoPqJQzaXLiXbt2slVq1YVfGAe0k9kcHz/CWo0rFqkEEh5sHn5Np65/FU8rmBHKxSRb+68JZCR8tDIu4Li8QWRnpzB98MmM/WzmSHOXTEp+mGRXOitoEETq/vp8Jchn3f/+g/pSlXbIqwMmzGYGg2rUSVhGTJ9cB6HBYhITb44T3ri5I+nM+7F70KeRqx2K/Wb1+Ht6S+xfPoaPnzoyxyZZSEE1z7Uk98+nh5yw7BF2PjpyFgckYXv96r6dsDxwjWmCSavpEQBiCjtcxFRWjvGqEHaClvPpsPnEbZPcdCcEYj4iYjcq/sw5Oj9CwdYWpfKjSI/1LQXwTmFkFRS83koiVPL9NpnE0KI1VJKXUGvs2ql7/V4GXn/5yyctBSLzYzf6+fGZ3tz5ys3VchCkUO7jvBc9zdCHD5oD+IxVWPxuj1kpzlDbgJel5fZ4+fjc3t56suHCn3NmCrRINBdzRfk8IGwssoms8LqOf9wSd/2QeO6qZCA1+2jUdtzsEfYUE9MCXX4gGboqkADkVP0HXQV1etX5Yd3JpN8MIXz2p9Lux6taNzuXBq21EIfPe68jIuvu5B18zdisVlo3bU5iiLYtGQr21bvxJWlhZnMFjNPfDGwSA4fAF9hewXkDtMENoBdP4Z5vzqYz0Op8l3IsJR+LdykxJ+6KYpIkPqfdzAKhXlCUZ2/Q/pgtBtVoGF8/Jdlu+J2/Ylu7YBvB1LNKJLkhoE+Z5XT/+ypiSz+eVmQzv3P70+jap1Eet3brYCzTz+/fvBH2IYkqirxZLt5Z/ZQkmpX4fker7NvS7D4l9vp4c9vF/PA+3cVqTbg/I5NcETNwZkZnG4nhNDNrCksXo+PsS98w+yJC/D7VC7uexHntK7HpiWhmUPV6iVhP9nIPczKVUP/tY7XtqPjtfkrk0bGRnJxn2DJ6hFzX2bJ1FUsmbqS2MRoet3bjXpNi1DSG0BYzkOGjbsHMNUHcxNNMM1UQ0tztF6K9K3VNlpzMnSsaI4u703XGmjsEoyaPQky3g3E7iUy4iZE9AsQcTtkjUevCU0wskBNIenbCWnPE5R1JLOQJ+6Gqn+VnbSCsIb/SPP9OzEoLGfNp+j1eJk1fn5OQ+yTuLLc/PDObxXS6e9cvzvfTBShCFKPpnF+h8a6PXdBC7ekHEkjItrBlhU7yEzJpGmHxvk2W+/Uux1V6yWxZ9O+oC+YUARms7lQnbHy4s5289O7U9izaX/Ok8uciQuITYrBFmHF4/LmPKnYIqw8+vG9p67ruAHp+Vun3N8E1jCNdIuJyWyic7/2dO7XvuCD80GYqiFtV4J7JmG9lFIPJf7j0PGEr5GZX4LzV+1cx3VgbgZpz6CFfk5+/j7I+hLVu05ryyjsSNdcSH+LoNz67J+QgIh+EakeAefvWsWudIGI0Y6V2WhfdzPEDC/Qacu0l9FNM5UecP8N9svzPb/YOG6CrLEE37jMYL24VGUvKjNnjdN3ZbnDhidSj6Xrjpc3TS48l83LtmlN0nXwuLzM+XoBw+8cjdcd/nHc5/Vy57mPknYsHaEIfB4f97x9G9c/cY3u8SaziWadmrB38/6glb2qqlSrlUTa8XQ8Li8+T+GFxSSwfU2w6qjP6ycrLZv+Q25g57rdbF+9k9pNatF/yPWc3yGXyrbtcrD3AedktNWuWdM7i/9UqzCtoIi4kcjMRpD1kf4B3uX65wm7VocQPShoXFpnIVMeBt9mTonMucGzEpnxPiJmMDLzE8KJnxH9LErsO8ioZwO69/W0NEz3n0j3AlASEY4b9FtF5rZDurXMGV3coNseu3QQUQ9qxWyelYFsJkCpofXoNSgVzhqnHxUXSXy1WI7tSw55rVnHCiXjn0O/x69m+pdz8XtDm7LYIqwIIVgyZVXYm5ktwsbtL9/Ay9eN4MjuY0EOfPyQH2jUpiEtL9WPv/41eXnoRrHUiqR+OjyW7HQnK2et4/OnJoQ8ZQhF0Llfe/6avCLHNr1USNBuxscPJDPkh/AqkEIIROxryMg7wL0ElGiwdUcoUWHPqQgIYYKoh7RKVb0YuSiiHIdSPdAQPu9n6QbnLxAzGPyHwlmjdakyVdcqfHNX+dp7Iuw9C2WClBL8B9Bcg95ixK8bciosUmoFaWSNATVFS4GNeRYRkJUQwopIGBdoJblFa/VoubBC7smdqZw1gmtCCAaNvg9brgIfxaRgj7Jz3/CCS9bLAlVVWTJ1Je/c8RGjHviCTXmqYpNqV+GjJW9xQbeWmK1mbA4r8dVjaX15c7re1hkpZYjDVxRBRIyDeufX5qkxD9DqsuakHEkLicV7nG6mjJ4R1jYlny+R2WqmWr0ket3blQu6tsgRljNbTFhsFobPGsr2NbsKtfFri7BSp0mtAo8DEOZzEZF3as2383H4Ukr2bNrH9jU6aZllgFTTUdNeRD3SSmv4nfIY0n9Es1mYAjnpeQXQ7BDRv/DXkBKZMZKwG6wndezD5tBbQSl+Lrv0rEY9fh3yyHnI5BvQryBGW3XrFIdJ3x6kay7StyP/62R+BOlvgH+31sDFswCZfCPSF9xIT1jOR0T0Q1gvMhx+KXPWrPRB29wbMfdlvn3rVw5sP0iTCxtx+9DrC+10ShNVVXn9hvdYPUfrsCUUwZ/fLqL/4Ou59cVT1Yr1zq/D8NlDQ84fdvuHunF8a4SNRz++N0eGYfWc9ZiU0Hu3lOEzZwC69u/MtM9mhVTdNr/4vJxMFpPJxOtTnmfD4s2sn7+RmMRoLru5E7GJMVjtBYddhNAqfq+4/dICjy0sezbv5+XewzlxOAWhCCxWCy988xgX6mrnlBwpVeSJ28C3ixxH6J6DTF4LSXMCoZrBSP+hQEjCqjloezdE1IOFv072twH5BT3EqdW17fJA79s8WM4Nm8qpez01U8v+MdUE327kiQHkxNFlJlrGTt7GNVZE7LDgeaQHmfoEuBcHumr5kNY2iLhPEUrwk45UsyBrHMHx+kAFc9bnIXMblA1nldMHLTPlrd9fLG8zWD17Pavnbjil/xIQFvvmjZ/pftdlYStYT1Lv/NpY7RbddM4aDU+JujXt0DhHnTM3tggrna8Pv1l512s3s37BRg7uOIzb6cEWYcUR5QhpHymEoOWl54eEia55sAdjn/8mqOhKO0G7WSDg3NYNeHb8w/luKhcFr8fLM5e/StqxtJxwmBMXr13/LuM2fkC1evpFSyXCsxT8+wle+fq11EjXdHD0QygRiISvAs3L94C5EcJUM2gaKf1aAZNzMmDWZBxsPU+tYrO+JLzwWdSpDlWuP/QP8W5EqpkFhsSk9CLTXwfnbwHFTwlKHUKfMPxoTt8KCFCiIOpFhK1T8HyZH2kOP3dXLc9qZMbbiNg380y5R8vACdn3VsFT+GY4BiXjrAnvVDT+/m1FkKrlSRSTwurZ6ws8v9e93TBbg+/JZquJmg2r0axTk5yxiGgHA9+9I2cPALTCLYvVzNxvFjPmuf9x/OCJkPkjoh18umo4Qyc9xYA3b+XpLx/if/99UmjHee1DPYirFhsyflKE7tfj4xm9fFiJFDnzsmrW+pyG8rlRfSozx88rtesE4dse0NzJg8xGercGDQlzfYSti47Dl8jUR7SMGM8S8CxCpj6PTHvh1EFqShgDBCRORZgbaL/694c5zARq6L9ziNkZIwLFT24to0c6wa+3j4C2J5HwNSLpT0TS3ygRvUOPyf6RUHE4Nzh/C03/NVXXsn9CLxRWWsKg9DnrVvoVBUe0Q7fCVVE0+QE9jh88wdevTmLF9DVExkTQ97GrWDljLTvX7w70pG3Hk2MeCIlx9n74Ss5p3YCpn85k35YD7Pp3H9kZLrYs386OtbuYMfZPRq8YRq1zg2OxiqJw4ZUXFEnD5yQmk4n05FCtealK/lm0qVDhn6KSejQNv84+gtfjI/lAwQ6vWJgb5jQDDyYCYW5UuDk8K8CzjOCVvBNcM5DeuxGWplqs3qtTfW6qg2LOFZ60tAL3UUKdtNCcaj5I6Q046byLkTApp9KDMDfUunKFnTRcTYAX7WnhlIsRSgLS3h1ccwm+Udi0GgaD04Kx0i8jet59ORZr6D1VojVgyUt6cgYPtXmO2RMWkHwwhb1bDvDzyN9pfOG5/Jb6NVMz/scrvzxDdIL+43uzTk144X+PkZXuxOfx5dxsfB4fWenZjH3+21J9f7s27MGZGSYcUUb7bs0vOQ+phjp9R5Sdtj1al81FrReDUpXg9ZECSgTYCyfDoNUg6FXg+rXwESBiXgBO9gg4iR0RE7zfI6IeA2HPc5wDoh4vuGBKOim8BIQdHNfn7/BBa6Su9w9uaaG7xyBi3wlsfFu1H6UaIm4Uwtq6kHYZlBTD6ZcR9ZvV4cFRd2O1W4iIdhARo/28Oe2FU5WouZj66Uyy07ODslHc2W5mT5hPVlo2x/Yl8+KVb3Kl9RaujriN9+75hKy04D6oWWnZHN0T2l9WqpK180qvWbuqqrzY623dBaJQBG27t8rp/lWa1GlSi663dQ5qU2lzWKlzXi0u7nNhqV8PtOwcUeV7sPUgp+G3tTOiys8hG5Vh51DiCc3uAbCAiNOOsbREVJmkXcdUG6yXIhImImzBfROEpREi4UewXgYiHsxNELHDUCLvLoQh0aCE2UsyNwdzCzRFzjiIegAR83LBU8YM0XSBOHnDsWjaPjGv6x8vbCixbyKqrUZUXYRIWhQkqmdQ9hjhnTLkmoHdufSGDqz981+sdgttu7fEatdfja1fsEl309Zis/DvX1v46JEvyTiRiVQlHr/KvO/+Yte/exm9/J2ccI/VYUUo+svsyLjitXDUY/Oy7WRn6GvHWG0WnhpT+DaCReWpLx+k9eXN+f2L2bidHrreegnXPtSjTG4yJxFKAiL+g0CMWhZddMx+DWR8oDOxAHuPU79amiD0KnjznmZpgkj4Iuzr0r0UmfWFlm9vvQgR+SDCXEeT2YgeAmnPcirEo7U+FLFvFktTR5gbQOJMLfvI+w9YmiIibg9J65RqNngWa/sjtou1JwidPr8GZY/h9MuYmIRoutzYscDjajWqzj+LNoXsAfi8frat3ok72x0suObxsXfLQTYu2UrzizV9d6vNwqU3dGRRQH/oJLYIG/0eK44ipD7OTFfY3OlmnZoEtV4sbYQQdOvfmW79O5fZNfK7dnFiV8KUBPGjtdRGZODHqlUcl3IBmpo9GdJfIcepO/cjXTOhymSEuS6KoydSiUNmfQK+vWBpjoh6DGEpfgGjMCUhop8I+7p0/4VMfRQtsCC1tM6YV1ACjWgMTi+G0y9jdq7fzeJfl6EoCl1u7Bg2m6XvY1cx95vFQXLBZquJBs3rkHo0TTdnX6qSfVsO5Dh9gMc/u5+04+n8s2gzFqsZj8vLFbd3pu/jV5fae2py4TkhYm0AFpuZy2+9pNSuUxqoqsqR3ceIiHEQmxi+1WJZI2ydoepS8K4HFCQ2ZPZYZNpQLf4dNbBQLRDzQ0ofZLxN8EatXxNKy/wIEfdewJb2CJt+Oq/0HwOZBaa6pSKjLNUMZOojobpK6a8hre0Q5volvoZB0Si20xdC9AVeB6KBP4GBUkq/EKI58C0QB0wFHpdSqkKILsCXaP3Vxkop3yqp8RWd8S//wC/vT8Pr8SEE/DhiCne+ehM3PxvaVaje+XV49ddnGXnfZ6QdT0dKyQXdWvD814P489vFLJy0NEQ/XghB3TwKkY4oB8NmDOHgzsMc3n2M+s1qk1A9Pl87j+w5xqR3p7BxyVbqNKnJTc9eR6M2DcMe//dvKzGZlRDNIJ/XT+cbil+iX9os/2M179/3GdkZTlS/SovO5/PSd4+fVucv/YeRWeM0iWhTDcAMnr+DJZD9/yHdsyDhW4SleO0otXkOhkmJVLUMonztPI5MfTxwUzJpG9UxbyNKKqzm/hOk3tORH+mcgoh+vGTzGxSZYjdREULcA/yAlps1A/hKSvmdEGIRMAyYDcwDRgFTgG3A9cBOYC1wk5RyXX7XKG4TlYrArn/3Mqj9iyHFS1a7hbEbR1GjQTXd86SUHNufTES0I6eoKSsti7saPUbGiQzUQIjHYjPTsGV9Pl72donK1PdvO8ij7V/Ele3G7/UjFIHVbuGVn58Jm8r57BWv6faadUTbeWPKC7S6rASOq5TY9e9eBnV4MegJyWQxcU6r+nyy4vSId0nfXmRy38AqtxDidZYLUaoUP8tKqunIo53QlXKwtECp8ov+eVIik68D3448dtoRib/m6OIUy6bs75HpwwhNExUQcRdKzEvFntsgPPk1USn285uU8ispZbaU0gusB5KEEElAAynlDCmlH23FfyXQFjgipfxHSpkF/BwYP2v5+7cVeHVUKqWEpVPC38iEEFStkxhUxRoZG8nHy9+m3ZWtMVtM2CJsXHFHF0bMfbnQDj/teDrTPpvFd2//ytaVp/RRxr74LdnpzhyJ55OVwx88OEZXWz/9RIZuY/OTWMogP784/PrhH0ESEwB+r5+9m/az69+9p8UGmTFSC5UUxuEDeNcjvZuRzt+0frdFXJAJJQZs3QjJFBIORGQ+m+u+zVq1bIidHmTWxCLZEIK1M/ppXnaEXgtKgzKnxDF9IURNoB/QA6gN5P5G7QeuBuoAe/KMN0EHIcRAYCBA3bp1S2peuWEymbRMmjxp0UIRKOai32trNKjGW78Xb1W0Zu4/vNxnBCDxun189/avXNz3Ip6f+Cj/LNio61xOHE4lPTkjKBTy47tTmPjKjyhhMoQcUXbOu6hwq8JD/x3h55HT2LZqJw1b1efGp6+lduOaBZ9YSA7/d1RXEM5kUTi+P5kGzU/D35ZnKYVqX5iDRCbfHJAUlmBqCAkTNWdeSETsMGTac+BeEGiGLiFqECJXllAI/iNoWjt5UcNXABfWHnNtZNRAyPwSrSBLAg6wdQdL/k1wDMqGAp2+EOILtJV6bu6VUq4XQrQDvgOelVJuF0K0J/ivXEVze9Yw4yFIKccAY0AL7xT2jVQ0Lr2xA9+8+XNokxQpuaSEDTyKgtfj5fUb3w/aD3Bnu1ny2wr+/m0l0QnRZKRkhZwnBEGVw+sXbOR/r/2EN29aqQB7pB2z2cQbU19A0RF/y8vO9bt5svNQPC4vfp+fbav/Y953ixkx9xWati9klWsBtO7ajE1Lt4akwXpcPs69oEGB50s1BZn9k7YKNjdHRBSiUCkvShz4w8kr5MWE9rXwnloY+7Yh019FxI0s9CWFEoGIH41UT4D/GJjrIUT+rSClUkWngQ2AHayddMaLhhI1CGntjHT+CngQ9qvBekmFU8+U0q+ppp7lFPgNlVI+IKVsl+dnvRDiUuAr4EYp5c+Bww8BuSUtawP78hk/a6l1bg0Gjrgdq92C1WHFFmHFarfw2Kf3Fyi2Vpr8+9cW3ZW8K8vN7InzueHpa0KKxax2C5fdfHFQTcGUT2aGbCQDWKxmbh9yPT8eHEPjtoXLPvnksa9wZrpyCtFUv4ory83Hj44tylvLl2sf6klkXCQmy6kvsT3SxrUPdie+Wly+50rfLuSx7pA5WhM4y/wQeawH0lfEsFDEvWhVtvkh0NZEeg3TveCapWXlFBGhJGj5/AU4fDV7MpzoT2gIxgJKHCLiliJfW9cea2uU2NdRYt9B2DpXKIevZk9BPXop8khT1KOdULO+L1Hb0IpOScI7nwO9pJQ5YRsp5V4hRJYQ4jJgMXAHMBhYATQRQjRBC+30A3qV4NpnBNc90otO113E0qmrEIrg4j4XFphJc7q55oEeHNx5hKmfzMRsteDzeGnboxWPfXp/0HHpJ/Rlmi02C43anhO26EyPzXn6Cpxkx5pdqKpaqKeFgohJiObzNSP49q1fWTZtFVHxkVz/xDV0v7NLgefK9FcC2TUnv/gukB5k+uuIhMLfmETEjUj/Lsj+JiC57EKLtwc2NS2tIHIgwtoGeezSMBI4fooWIio8Uj0B6S8TKpgmwNYDETu0QjYi1xq97AIsCHPJBP1U53RIH0rOv4l6HDLeQSIRkbeV2NaKSLGcvtCaVTYG/sx1x54npRwI3AVMREvZnCCl/Ctwzr3ANLRlzfDcN4uzmaTaVej9cOG6FpUFzS85T3dVZY+0cUX/S5k1fj7/Lt5MrUY1aN21OX0e7UXNc0KFuy7p254ty7eH1Av4vX6ati9adocj2kHGiVCxttxKoaVBQvV4Bn18L4Ny9eMtCCllIL0xrwdWNYXMIiCEQMQ8j4x6QMuMMdVEmGoi1XRACSrMktZLwT2HEAdvaVl2TcjdCzR1Tr2bjSkJEU6yoRyRnjXI1Ce1LmGoSFNdRPzHCHP4FON8yRxJaGaRE7I+BsPpn0JK6Qx3rpRyDdBCZ3wm2o3CoAzx+/xsXKLFsptd3ARHpJ2hk57i1b7vIqXE6/ZisVvpdN2FLPp5KStmrM3R/D+44zDbVu7k/YWvaZr4ubjynq7MGPsnB3Ycxp3tRgiB1WHhwZF344gqWsPq3o/05Of3pgWls1odVq4eeEUFeey3ELr6RVutFwOhxIG1Xa7fQzdmRcyLyOMrA8JsgScCYUHEvBlybOkhwjxdVIR/g1Ck/zgy5Z5g8Tr/DmRyf6i6sHg3x3DtJ9UTSOmt0D2ai4tRkXsWsXXlDoZcMwyP24tA4PP5eerLB+h6a2e+2f0pC35cQmZqFu16tMJkNvFE56HBG7xODzvW7WbBj0vodluwzIE9wsZHS99izteL+Pu3FcRXi+Xah3oWa+P1jqE3cmTXMRb+vBSrzYLX7aXDNW255+3CtxcsK4QQSMe14JxKcL67FeyhRXWldl1TTUiarfWP9a7XhNQcN2n9bnWQagrSOQV8exDWtmDvUXSnZ7sM0BNVsyIc1xT1LZQ50vkbyLz7HhJwaU8t+WUohcNUB/z/hY4rSWelw4cSFGedDs7k4qzTjcfl4eaaA8lMDc7EsTmsfL723ZB0yMkfTefL578J0ug5iaIIzuvQiJe+faJsulEFOH7wBAe2HaLmudVJql12ej1FRaqZ2orSu1VLY0KC+XxE/LhCK2uWqX3eTcgTtweau7i0ZidKNUSVn4qU3gnaJibpga5c+AETRA5AiX6qtM0uMWram+DUaylpRUS/gIgsei9s6fpTCxcFhXjsEPMqSkS/cKdVeMqkOMugYrFixlrdvHSfz8+s8fNDxuOrxWK26qenqapky/IdPNF5aJk2Hk+smUCry5pVKIcPIJQoRMKPiCr/Q8S8ikj4FqXK9xXC4QPI1GcCfWxP9rTNBv9+ZOYnRZ5LibgOkTQHEf0sIupJROJvFdLhAwjbhehLVHuRlqbFm9PeTUuJNTUEzNrKP/btM9rhF4QR3jlLyEzNRtVpMOL3+klLDs286di7HeZHwv/zq36VrLQsVs5cR4dr8pZpnP0IIcDSUvupQEj/cfDrpY56tZ69MUXvDy1M1SHyzpIbV8ZIaxf0q5uF1pTeWry/U2G/AmG/okS2nUkYK/2zhAu6NtdtJWiPsus6bZvDxvvzX6VGw6qYwlQI+70qR/ceL3VbDUrAyWbmupydMeiTCP+uMBr8aviG8QYhGCv9s4Rq9ZLo+9hVTP1kZk42jj3SRtOLzqX91cHtGbPSs5kx9k/WzP2Htj1aYY+wMe2z2SHicIpJ0LhdMVPhKhBpx9NZ8OMSMk5kckG3FpzfsXEFyRIqOkKJR1pagHcdwemdWo9c6T+KMFUtJ+uKhlQzkdk/as1VTDUQEXfk38hFOECGqVkQkfrjBiEYG7lnGatmr2f6l3NxZbu5/JaL6XrrJZjMp2L36ckZPNT2OdKOp+PO9qCYFMxWEzEJ0aQdT88RKbM6rLS45DyGzRxyxjpIgLXzNvDydcO1jmNuLzaHlXY9WzPkxydD0lLPFKT/ADL51kCueu4NSIsmrlblV4S5YutWSTUNmdwH/Mlo70EBrBA7HMURvm5TPXY1+HcSfMNzIGLfQDh6l6nNZxL5beQaTr+SMea5r5n80Qx8eRRAYxJj6H5HZxb+tBSzxUyv+7px49PXYrGeuSEDn9fHTTXuDykEs0faeHLMg3StYA1fioKqeuF4F62CNAgFbD1R4j8sF7sKi5oxCrLGESIDLWIQVZeGTZeUvj3IE3ecqpiWfnD0RcS8dkYvTkqb/Jy+Ed6pZPz928oQhw/gcbq5emB3Hnz/7tNvVBmxZfl23ewjV5abWRPmn9FOX5CFVNN0Xil65XC54JqLru4/fvBthzBhHmGuB0nzwbNMu+FZ2pRYiqGyYTj9SkZkrH7aod+n4oguWmVthSefld8ZvyrMT0itAurlhKDE6uvsSh+I/GsNhDCB7eKysasSYGTvFIIda3fx1+TlHNp1pNxskFIy/cu53NvsSW6p/QCjHviC5EOFle09Rd/HrsIeGZwBYTIrNG7X8LSqf54OmrZvFLSfcRJ7pI2ed5ewDWA5I4Q9UIGatwrXAREVP/1SRN6lbcwGYQJzY4S5tu45BqWDsdLPh/QTGbx45Vvs3bwfxaTg8/i4pF97npvwqK4zKUs+HjSO2RMW5MgmzBo/nyVTVjJu4yhiqhR+ZXfF7ZeybdVOpn85F7PVjOpXqV6/KkMnPV1iG/0+P1tX7cRkNtGoTYNSUcssCSaziVd+eYYh176DVCU+jxez1UL7q9vS5aaO5WpbaSBi3tCUMj1rAiqeHnBcizgDnD62HhCxEbLGa81epKoJ0sUXvcDMoGgYG7n58ErfEayYvgZfrkYotggrd756Mzc9c/oyBY4fPMFd5z4a0hDEardw2+B+9B98Q7Hm3LZyJ1VqJdC4bcNihTucmU4W/LiEPZsPYDIJpo/9E9WvIlVJRLSD16Y8T5N2hdPYBzi2P5m/J69A9at0vK5d2D7CRSUjJZNFPy0lPTmTC7o157yLSqdRS0VB+naD/wCYG50x6Zonkf5k8G0AJUmTujjTw24VhEqXvZOd4WTGuD9ZOXMdVesm0ufRXjRsWa9IczizXPSrMkB307Na/SS++e/TIttVXJZPX8Ow2z4gS6c37QVdmzNi7iunzZaTHN59lEEdXsSV5c6pC8hLZGwEPxwYE9KkRY8Z4/5k9KBxQWN3v3ELNz5tpOEZGBSVSpW9k5GSycPtniflSGpOHvq87xbz3IRHufSGwj/Sh7QFzIUzM6/+dtlStU4VfL7QohTFpFDz3FDt+9PBqIFfkH48A1UNv2hQ/SpLfltB1zyKnXk5fiCZ0YPGhTzJTHj5R9pf3Za659UKc6aBgUFROes2cn8ZOY3kgydymn2ofhV3todRA7/A5y1827nohChdhUnFpNC+VxudM8qOBi3q0aB5nRCBNIvNQt/HrjqttgD4/X7Wzf83X4cP4PP6STuu33ErN3//tlI308bv9bPo56XFttPAwCCUs87p//3bypyq0tz4/X72bNpf6HmEEDzz1cPYI22YLdoDkdVhJaZKFAPeurXU7C0sb/3xEhd0a4nFZsbqsJJYK4FXfnmGeucXL0fZ7/fz0/tT6d/gYfolDuDt/h9wZM+xQp9fmNirYhK0vrxZgcdJVYJOmFFKqascamBgUHzOuvBOZFz4PPRwOerhaH7xeYz5532mfDKT/VsO0rxzU666vxsxCac/DzqmSjT3DevPrAnz8bg8XHlPtyJtkubl/Xs/Y9HPS3OeiOZ//zeLflrG45/dz5X3dM3XqZtMJjpe246l01aFlV62R9q4pF97GrQoeC+l03Xt+PL5/4WMW6xmOl/foZDvyMDAoDCcdU6/3+NXs3Pd7qDNRcWkUL9ZHarXL3pmQ40G1XjwvbtK08Ri8e1bv/D927/i9fiQUjLn64X0HXQV9w4rerepo3uPsWDSkpB9C7/Pz0cPf8nOdbt5tIC+so99dj//bdhDypFUfG4fJouJqPhIqtZJxGq30uu+blx2c6dC2VO1bhL3vtOfcS98h9/vBykxW8zc9Nx1NGhesTVkSop0zdV08P0HwdICEf1U/qJjBgYl5KzL3pFSMu6l7/j1wz+wWM2oqqRqnUSGzx5CYq2K1ayjsBzYcYiBLZ8O2ei0Oax8vOztQq2mc7Ps99W8c8dHZKVl675utVsYu3FUgSmTfr+fVbPWc2DbIeo3r0Prrs1LlJt/YMchFv20DL/fT+d+7YsdujpTULMnQfpbQO6sLAeiyveG4zcoEZUqe0cIwX3D+nP9E1ezZcUOEqrH0bjdOWd0/u+yaavRuzl7PT7+nrKyyE6/eoOq+W5qm8wm/v1rS4FO32Qy0f6qNlBKe8m1zq3BrS/2LZ3JKjhS+iHjXYIdPoALmTESkTC2PMwyqAScdU7/JPHV4uh4re6N7ozDZDEhdFbQiiJyNpmLQv1mdYivFsfhXUf1DxCCuKSi9Vo1KCLqcZB6qb8SvBtOuzkGlYezLnvnbOSSfu11s1sUk0KXG4suJ7Bk6kpOHE7VfU0IcETaaHNFxWoTeNahxIV/zVQz/GtnCdJ/GDVzDGr6MKT7L2S45igGpY7h9M8AEmsm8OSYB7DaLdgjbdgibFjtFh76YAA1GhZdquCHd37D49STtYVq9avy7rxXT7u2UGVDCBtE3ATkVct0IKIeKQ+TThvSvRB5rAdkfgTZ45GpjyJT7kXKwtfRGBSfsza8c7Zxxe1daNezNUunrkKqkg7XtiWhenyx5jpxWF+d0+qw8Pb0l6jTxKiAPR2I6Be0brfZkwCh9X+NfuasbtItpQeZ+hRBHb9ktiYa55oKjn7lZltlwXD6ZxBxSbH0urdbiedpdVkz5v5vUUjhk8VqKdaTQ1lydN9xdqzZRdW6iZzTuv4ZvSGfFyEsiJihyOhntdaHSiJCnOVfSe96glsdnsSJzP4NYTj9Mucs/wsz0OOOl2/k799W4Mp04Q9o+tgirDzw/p3F2hguC1RV5YMHvuDPbxfnSEDXaVKLYTMHE5t4dm0yC2EHU/loKJ1+8mm/GaZFokHpUuyYvhBikBBioxBivxBilAgswYQQzYUQ64UQe4QQHwshlMB4FyHENiHELiHE4NJ6AwZFp3r9qnyx9j16DuhKrUY1aN21Oa//9jy97in5U0Rp8fsXc5j3/d94XF6y0524stz8t2EPw+/4uLxNMygJlhZhun45EBE3nnZzKiMl2cj9RUrZDGgE9AFOiqx8CrwANARaAr0DN4SxwA1Ac+AuIUTrElzboIRUq5fEIx8O4ObnriMixsFfk5eza8Oe8jYrh98+np7TMOYkfq+fdQv+JSMlM8xZBhUdIUyIuM9BRIGIBGyAHRxXg61neZtXKSj2s7yU8mDgf6uiVZjsF0IkAQ2klDMAhBDfAlcC+4EjUsp/AuM/B8bXFd90g5LgzHLxeKfBHPrvCK4sN4pJYfaEBTw17iG63lL+DcOzdXoHAAhFwZXlJjo+6jRbZFBaCGsrSPoL3H+CmgLWjgjL2dXYprhI33/I9HfAu1y7MUbcgYi8X+sLXEqUJLzTSAixG9gMjJBSpgK1gb25DtsP1ADqAHt0xvXmHSiEWCWEWHXsWOFVHw2Kxu+fz+bAjsM5GkWqX8Xt9PDhA2PwuMP3EsiNx+Vh+R+rWfzLslJffXe4pp1u2mhc1RgSa51dvXwrI0KJQDiuRUTeaTj8ANJ/GJl8I3gWgnSCegwyP0WmvVSq1ylwpS+E+AJom2f4XinleqC+EKIO8IcQYgfgJXhrXkXreW8NMx6ClHIMMAY07Z1Cvg+DIrLo52X6ufoCtq/+j2admuR7/obFmxna+x1NHkKCz+vjoQ8GcM3A7qVi352v3siSqSvJSsvG4/RgMpuw2Mw8PfbhsyqDx8DgJDJrQqBKO7fbc4HrD6T/SUQpbfYX6PSllA8U8Po+IcRUoAMwCcid5F0b2AccCjNuUE5EhZGZVv0qEdF6G22ncGW7GXLtsJAQzOdPTqBZpyalooyZUD2ecRtH8ceYuaxfuJFa51anz6O9qN347K9WNaikeNehrZvzIGzg21lqGV4lCe9cHPhvFNAdWCWl3AtkCSEuE1oQ6g7gJ2AZ0EQI0UQIEQn0A34tsfUGxab3I1dijwzuXSuEoErNBOoX4LRXzlirKwvh9fiYPXFBqdkYHR/FLc/3Ydj0wTz60b2Gwzc4uzE3BnRi99IDptJTnC1J9s5LQoi9wGrgRynlgsD4XcDHwG5gkZTyLymlB7gXmAZsBD6WUlacVJFKSIdr2tLnsauw2CxExDhwRNtJqlOFt/54scDwiTPTharj9FW/Glau2cDAIH9E5AAQ1jyjNrC2R5hLr6/EWaenb1A0kg+lsGnJVmISo2nRuWmh9PCPH0jmznMH4c2z4WuPtDF00tNc1OuCsjLXwOCsRnrWINOHgu8/wASO6xAxQxDCUaR5KpWevkHRqFIjvsgtCRNrVeH2odfz3duT8Tg9SCmxR9lofXkL2vVsVUaWGhic/QhrG0TiH0jpBCxlIsthOH2DYnHbS9fT+vLmzBw/H1eWmy43dqRj73Yl6pxlYGCgUdSVfVEwnL4BoLUq3LBoM7FJMbTr2QqLtWAdlPM7NuH8jvmndhoYGFQsDKdfyZFS8uHDY5gzcSGKSUExKVjtFt6b92rYHrV+n5/05AyiE6IqjECbgYFB4TCexSs5Cyct4c9vFuNxeXFluclOd5J2LP1U4VUupJT8+uHvXJ90D7c3eJh+VQYw8dVJqKrR9cjA4EzBcPqVnGmfz86RYjiJlJByJI3d/+4NGp81YT5fDf5Bq5J1eXFmuvjpvan8OGLK6TTZwMCgBBhOvxKxZMpKHr94MHec8wgjB37O0X3HcYdpm6goSshr37zxc4jypTvbzY8jfgt5KjAwMKiYGAHZSsJP709l4iuTcpz27Anz+evX5fR5rBe7/92LOzvYwZvMCo3aNAwaO3EoVXfu7HQnPq+vUJu/BgYG5Yux0q8EOLNcQQ4fwO9Tyc5wkno4jfrN6mCP0vR2zFYTtggbz//vsRCVy3rNauvOn1S7iuHwDQzOEIyVfiVg35YDmMyh93e/188/izbx+dp3+XvyClbNWkdCzXiuvKcrNRqE9sp94N07GXLNsKCwjy3CygPv3Vmm9huUH9K9DJk1FtTDYL0EEXkvwpRU3mYZlADD6VcCEqrH4fX4dF+rWi8Js8VMl5s60eWmTvnO0/ry5rwzawjjBn/Pno37qNGwGne/cQsX9mxdBlYblDdq9iRIfwutRxLg24V0TobEaQhT1XK1zaD4GE6/EpBYqwqtuzRj3YJ/8bpPOX9bhI2bn72uSHM1v6Qpoxa+XtomGlQwpPRAxjByHD4AXpAZyMwvELFDy8s0gxJixPQrCYN/fJK2PVpjsVlwRNmJjI1g0Oh7aXVZs4JPNqh8+P4L9wJ4/jqtphiULpVmpa+qKqtnr+efhZuoUjOBrrddQkyV6PI267QRGRPBG1OeJ/VYGunJmdQ8p5pRTWsQHiUBpH5IEOXMielLKbXOU85JIL1gvw4R0Q8RImFceagU33qP28vz3V9n57rdODNdWB1Wvhr8HcNmDimwLeDZRlxSLHFJseVthkEFR5iqIq3twLOC4G5ODkTUfeVlVpGR6S+Ccwan9iU2IV1/QMJEhKicgY5K8a6nfTaL7av/w5npAsDj9ODMdPHmzSONoqJywuP24sx0FnygQbkh4j4Aa1vABiIKcED0EwjbZeVrWCGR3u3gnE7QvoR0gm8DeBaVm13lTaVY6c/5eqFu5WlmahZ7Nu2nfrPSa0VmkD8ZKZmMGvg5S6etRqqS+s3r8NSXD9K47TnlbZpBHoQSi0j4Guk/COpxMDcqU8nfUseznOAm4wFkNtL99xlz8yptKsVKXzHpv00pw79mUPpIKXm+xxssnbYan8eH3+dn57rdPNP1VY4fPFHe5hmEQZhqIiwtzyyHD6DEg24TEisoVU67ORWFSuHxet3bDVuELWQ8oXocdZoYzbZPF9tW7WTflgP48tQM+Dx+fv98djlZZXDWYu+GbqNxTAhHn9NsTMWhUjj9q+7rxgXdmmOPtGG2mnBE2YmKj+SVX54psAm4QelxcOcR3c5aXreXPZv3l4NFBmczQtgRCRNBqQYiAkQkiFhE/GiEqXp5m1duVIqYvsls4o0pL7BlxXY2LN5CQvU4Lu57EXad1b9B2dGwVT18Pn/IuM1hpWn7xsWa0+P28tcvy/hn0WaqN0iix12XkVA9vqSmGpwlCEszSFoIvk1aCqqlGUJUbp0oUZGzV9q1aydXrVpV3mYYlCJDr3uHNXM34AlsrCsmhZiEKL7a8iHR8VFFmisrPZvHOw3myN7juDJdWO0WFLOJ4bOGGG0cDSo1QojVUsp2eq9VivCOQcXh5Z+e5uZne5NQI47I2Ai63NSRT1YNL7LDB5g0YgoHdx7BdTIV1+XFleliWP+PjFRcA4MwVIrwjkHFwWK1cOerN3PnqzeXeK75P/yN1+0NGU85ksrh3Ud1lUINDCo7xkrf4IzFYtNfs6iqxGKr3HFbA4NwGE7f4Izlmge6Y4sI1lBRFEH95nVIrJlQTlYZGFRsDKdvcMbS++EradujFbYIK7YIK45oBwk14hn641PlbZqBQYWlxDF9IcRNwHdSSnPg9+bAt0AcMBV4XEqpCiG6AF8CFmCslPKtkl7boHJjMpt47dfn2Ll+N1uWbyexVgLterYOafNoYGBwihI5faHVZT+WZ/hT4AVgNjAP6C2EmAKMBa4HdgJrhRB/SCnXleT6BgYA57Sqzzmt6pe3GQYGZwQlDe+8hubkARBCJAENpJQzpJR+tBX/lUBb4IiU8h8pZRbwc2A8BCHEQCHEKiHEqmPHjpXQPAMDAwOD3BTb6QshLgWaSim/yzVcG9ib6/f9QA2gDrBHZzwEKeUYKWU7KWW7pKQzp1mDgYGBwZlAgeEdIcQXaCv13DwNvA/0yjNuBdRcv6uAP59xAwMDA4PTSIFOX0r5QN4xIcQzQDVgcUCwzCSE2AL0BGrlOrQ2sA84FGbcwMDAwOA0UqzwjpTyPSllHSnleVLK8wB/4P/3AFlCiMuEECbgDuAnYBnQRAjRRAgRCfQDfi2tN2FgYGBgUDhKRXBNCOHLlbLZBpiIlrI5QUo5NDB+JfARWqhnuJTys0LMe4zgvYDCkggcL8Z55cGZZCsY9pY1hr1lS2Wxt56UUndTtEKrbBYXIcSqcApzFY0zyVYw7C1rDHvLFsNeoyLXwMDAoFJhOH0DAwODSsTZ6vTHlLcBReBMshUMe8saw96ypdLbe1bG9A0MDAwM9DlbV/oGBgYGBjoYTt/AwMCgEmE4fQMDA4NKxBnt9IUQg4QQG4UQ+4UQo0RAE0II0VwIsV4IsUcI8bEQQgmMdxFCbBNC7BJCDC4He/sKITYIIXYLIcYFqpYrrL0BG9oHVE9r5xqrsPbmRghxU8CWHUKIe8rbnpMIIWxCiIeEEJPzjD8uhNgrhNgqhOiVa/ydwN/4BiFEXh2ssrbVKoT4NPDvul0IcX1FtTVwfUUIMSdg71YhRM+KbG9uhBDThRBjA/9fdvZKKc/YH6Bm4L8OYBfQPPD7IjQxOBOwEOgDCGA70BKIBLYBrU+zvfcAEWiNZOYCt1Vwe0eg9UU4BtTONV4h7c1jezSavlMtoDpwGEgq77/ZgG27gcnA3Fxj5wQ+s2jgfOBg4O+kK/AXmk5Wd2Ddaba1OnBD4P8bA6lAk4poa8BGAdQI/P+VwKqK+tnmsbtnwK6xZW3vGb3Sl1IeDPxvVcAJ7C8NTf8ytPcrKWW2lNILrAeSKrK9wNtSyh5A1smBCm5vbnoCC6WUB6SUh9Ea+nQrR3ty0xr4MM9YX2CSlDJDSrkJ7cbQFk2naoKU0ielnIP2N1P9dBkqpTwspfw58P/bAB9wS0W0NWCjlFIeCvxaD+17ViE/25ME9MheQ1tkUdb2ntFOXwjRSAixG9gMjJBSplIKmv7/b++MXaMIojj8/UqNqCioBAuxsxAFU0QimMa/QETQIopipYWKIiktLSxEi/RWKSwURItICkEsLFS0SRBBgiikFbtn8eZ0b9m9HPHudo59Hxy3O8uxH+9253buDW+GjaRJ/At8Tsa+KZ5lsvUtkZvPX2riWudbbl+juev2IvAB2EXGrpJuS1oHrgN3K7yy8sUfAO7joygYsu9YdPqSFtL/ysXXETNbMbMD+HDzhqQTZFDTv843HZsCloFbZraSu28Fjfv2SW4+G5F1XCXdwZdGPd/DKQtXM7tnZruBeeBlD6/GfSXN4QOUxULzUH3/e2H0UWAVNf1Lx79JegpMA4s0XNO/zle+2thD4IyZvU/NdV6N+9bQuG+ffAdmC/v7gbfNqPRFv3GdxJ/8RoakR3ieZsbMfknK1rWImT2R9ICMYwtcA3bK1yPZgecntye3DoP1bSpxMaDkx0x634bf0LNp/yN+w3cSjZ0RwBo+KpgAPuPlR0fpW3nOXH0Lfl/pTuRm7Zsc9yafPXgy8gsw0ZRPhd8s3YncYylmW4FDaVvAaWApxfpU8TMj8pwunzNX1+R2ENiXto8Dqzn7ltwv4IncofqOxZN+D+YlHcaTuAtmtpza5+iu6f8aQNIl4Bn/avpvplb/ppC0BZ/9sCSfWQrwysyu5Oi7Adn7mtmPNG30TWq6aZ5gzhIzeyfpMfAJ+A1cNjNL0zpP4j9a68C5EasdBaYkrRbargI5uoJfky/k06F/Amczjm0lw/aN2jtBEAQtYiwSuUEQBMFgiE4/CIKgRUSnHwRB0CKi0w+CIGgR0ekHQRC0iOj0gyAIWkR0+kEQBC0iOv0gCIIW8QduKRimMBZ/mAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.26347406 0.42128621]\n",
      " [0.38478684 0.55713111]\n",
      " [0.78274477 0.53057454]\n",
      " [0.16242588 0.64871096]]\n",
      "0.8888888888888888\n"
     ]
    }
   ],
   "source": [
    "#数据预处理\n",
    "from sklearn import preprocessing\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.datasets._samples_generator import make_classification\n",
    "#支持向量机模型\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "#数据标准化\n",
    "# a = np.array([[10,2.7,3.6],\n",
    "#             [-100,5,2],\n",
    "#             [120,20,40]],dtype = np.float64)\n",
    "# print(a)\n",
    "# print(preprocessing.scale(a))\n",
    "\n",
    "X,y = make_classification(n_samples = 300,            #样本个数\n",
    "                          n_features = 2,             #特征个数 \n",
    "                          n_redundant = 0,            #冗余特征个数 \n",
    "                          n_informative = 2,          #有效特征个数\n",
    "                         random_state = 30,           #随机种子\n",
    "                          n_clusters_per_class = 1,   #簇的个数\n",
    "                          scale = 100,                #元素的大小\n",
    "                         )\n",
    "#两个不同的聚类簇\n",
    "plt.scatter(X[:,0],X[:,1],c=y)\n",
    "plt.show()\n",
    "\n",
    "#数据预处理\n",
    "X = preprocessing.minmax_scale(X,feature_range = (0,1))\n",
    "print(X[:4,])\n",
    "\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,test_size = 0.3)\n",
    "\n",
    "#向量机模型学习\n",
    "clf = SVC()\n",
    "clf.fit(X_train,y_train)\n",
    "print(clf.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 618,
   "id": "22da3d16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9777777777777777\n"
     ]
    }
   ],
   "source": [
    "#cross-validation(交叉验证)\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "#拆分数据并设置随机种子，使数据能够重现\n",
    "X_train,X_test,y_train,y_test = train_test_split(X,y,random_state = 4)\n",
    "#取附近5个元素的KNN模型\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "knn.fit(X_train,y_train)\n",
    "y_pred = knn.predict(X_test)\n",
    "print(knn.score(X_test,y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 632,
   "id": "a8a98f39",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.         0.93333333 1.         1.         0.86666667 0.93333333\n",
      " 0.93333333 1.         1.         1.        ]\n",
      "0.9666666666666668\n"
     ]
    }
   ],
   "source": [
    "#交叉验证升级\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "knn = KNeighborsClassifier(n_neighbors = 5)\n",
    "#数据分成五组(cv设置分组)\n",
    "scores = cross_val_score(knn,X,y,cv = 5,scoring = \"accuracy\")\n",
    "print(scores)\n",
    "print(scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 647,
   "id": "3144c162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#寻找最美n_neighbors\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data\n",
    "y = iris.target\n",
    "\n",
    "k_range = range(1,31)\n",
    "k_scores = []\n",
    "for k in k_range:\n",
    "    #model选择\n",
    "    knn = KNeighborsClassifier(n_neighbors = k)\n",
    "    #for classifier\n",
    "    #scores = cross_val_score(knn,X,y,cv = 10,scoring = \"accuracy\")\n",
    "    #for regression（平均方差）\n",
    "    scores = -cross_val_score(knn,X,y,cv = 10,scoring = \"neg_mean_squared_error\")\n",
    "    k_scores.append(scores.mean())\n",
    "\n",
    "#scores数据可视化\n",
    "plt.plot(k_range,k_scores)\n",
    "plt.xlabel(\"Valus of K for KNN\")\n",
    "plt.ylabel(\"Cross-Validated Accuracy\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 662,
   "id": "d33ebd5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import learning_curve\n",
    "from sklearn.datasets import load_digits\n",
    "from sklearn.svm import SVC\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import numpy as np\n",
    "\n",
    "digits = load_digits()\n",
    "X = digits.data\n",
    "y = digits.target\n",
    "#print(y.shape)\n",
    "\n",
    "train_sizes,train_loss,test_loss = learning_curve(SVC(gamma = 0.000001),X,y,cv = 10,    #模型以及参数\n",
    "                                                 scoring = \"neg_mean_squared_error\", \n",
    "                                                 train_sizes = [0.1,0.25,0.5,0.75,1],#标记点\n",
    "                                                 )\n",
    "train_loss_mean = -np.mean(train_loss,axis = 1)\n",
    "test_loss_mean = -np.mean(test_loss,axis = 1)\n",
    "\n",
    "\n",
    "plt.plot(train_sizes,train_loss_mean,\"o-\",color = 'r',\n",
    "        label = \"Training\")\n",
    "plt.plot(train_sizes,test_loss_mean,'o-',color = 'g',\n",
    "        label = 'Cross_validation')\n",
    "\n",
    "plt.xlabel(\"Training examples\")\n",
    "plt.ylabel(\"Loss\")\n",
    "plt.legend(loc = \"best\")\n",
    "plt.show()\n",
    "\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
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